GroupLens: An Open Architecture for Collaborative Filtering of Netnews (2023)

Paul Resnick*, Neophytos Iacovou**, Mitesh Suchak*, Peter Bergstrom**, John Riedl**

* MIT Center for Coordination Science
Room E53-325
50 Memorial Drive
Cambridge, MA 02139

** University of Minnesota
Department of Computer Science
Minneapolis, Minnesota 55455
(612) 624-7372

From Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work,Chapel Hill, NC: Pages 175-186

Copyright ©1994, Association for Computing Machinery


Collaborative filters help people make choices based on the opinions of other people.GroupLens is a system for collaborative filtering of netnews, to help people find articlesthey will like in the huge stream of available articles. News reader clients displaypredicted scores and make it easy for users to rate articles after they read them. Ratingservers, called Better Bit Bureaus, gather and disseminate the ratings. The rating serverspredict scores based on the heuristic that people who agreed in the past will probablyagree again. Users can protect their privacy by entering ratings under pseudonyms, withoutreducing the effectiveness of the score prediction. The entire architecture is open:alternative software for news clients and Better Bit Bureaus can be developedindependently and can interoperate with the components we have developed.

KEYWORDS: Collaborative filtering, information filtering, electronic bulletin boards,social filtering, Usenet, netnews, user model, selective dissemination of information.


Computer networks allow the formation of interest groups that cross geographicalbarriers. Bulletin boards have been an important mechanism for that. Rather thanaddressing an article directly to a known set of people, the writer posts it in anewsgroup, a public place available to anyone interested in the topic. The Usenet netnewssystem creates the illusion of a single bulletin board available anywhere in the world. Itpropagates articles so that, with some delays, an article posted from anywhere in theworld is available to everyone else.

Permission to copy without fee all or part of this material is granted provided thatthe copies are not made or distributed for commercial advantage, the ACM copyright noticeand the title of the publication and its date appear, and notice is given that copying isby permission of the Association for Computing Machinery. To copy otherwise, or torepublish, requires a fee and/or specific permission.

Recent counts indicate that there are more than 8000 newsgroups, with an averagetraffic of more than 100 MB per day[1]. The newsgroupscarry announcements, questions, and discussions. In a discussion, often called a thread,one article induces replies from several others, each of which may also induce replies.The January 24, 1994 estimates of netnews participation indicate that more than 140,000people posted articles in the previous two weeks. There are many more "lurkers"who read but do not post articles. Clearly, a lot of people are getting value from thesebulletin boards.

In fact, netnews' rapid broadcast nature and widespread readership has reshaped the waythe computing community works. System administrators depend on netnews to keep in touchwith the latest development work, the latest security holes, and the latest bug fixes.Researchers depend on netnews as a way of keeping up-to-date on new research directionsand important results in between conferences. Many others use netnews just to keep intouch with other people around the world, to learn about new books, new recipes, newmusic, and what life in other cities is like. Over the years netnews has become aprincipal medium for sharing among computer users.

Even so, the experience of using netnews is not completely satisfying. Almost everyonecomplains that the signal to noise ratio is too low. Writers cannot easily tell whethertheir comments are valued, except by the vocal few who post responses. Some seem not tocare about reader interest, only about their own right to write. Moreover, tastes differ,so that no one article will appeal to all the readers of a newsgroup. Each reader ends upsifting through many news articles to find a few valuable ones. Often, readers find theprocess too frustrating and stop reading netnews altogether.

Netnews provides two mechanisms that help readers limit their attention to articleslikely to interest them. First, the division of the bulletin board into newsgroups allowsreaders to focus on a few topics. When the number of postings in a newsgroup gets toolarge, it is often split into two or more newsgroups with identifiable subtopics. Second,some newsgroups are moderated. Attempted postings to these newsgroups are automaticallyforwarded to the moderator, who decides whether or not they belong in the newsgroup.Usenet propagates only those articles that receive the moderator's stamp of approval.

In addition, software packages for reading netnews (hereafter referred to as newsclients) provide other mechanisms that ease readers' burdens. First, most news clientsdisplay a summary of the author and subject line for each message in a newsgroup. The userthen indicates which articles she would like to read. Second, most news clients displayall of the articles in a particular discussion thread together. Some initially show onlythe first article in each thread, allowing users to quickly peruse the current discussiontopics. Third, some news clients provide "kill files." A kill file identifiestext strings that are not interesting to a particular user. If a user puts the subjectline of an article into the kill file, no further articles on that subject will bedisplayed. If a user puts the author's name into a kill file, no further articles fromthat author will be displayed. Finally, some news readers provide string searchfacilities. If the user is particularly interested in articles that mention"collaborative filtering," the news client can find them.

GroupLens provides a new mechanism to help focus attention on interesting articles. Itdraws on a deceptively simple idea: people who agreed in their subjective evaluation ofpast articles are likely to agree again in the future. After reading articles, usersassign them numeric ratings. GroupLens uses the ratings in two ways. First, it correlatesthe ratings in order to determine which users' ratings are most similar to each other.Second, it predicts how well users will like new articles, based on ratings from similarusers. The heart of GroupLens is an open architecture that includes news clients for entryof ratings and display of predictions, and rating servers for distribution of ratings anddelivery of predictions.

Related Work

The general problems of information overload and low signal to noise ratio havereceived considerable attention in the research literature. We use the term informationfiltering generically to refer both to finding desired information (filtering in) andeliminating that which is undesirable (filtering out), but related work also appears underthe labels of information retrieval and selective dissemination of information [2]. Inaddition, research on agents [12, 13], user modeling [1, 9], knowbots [8], and mediators[21] has explored semi-autonomous computer programs that perform information filtering onbehalf of a user.

Malone et al. [13] describe three categories of filtering techniques, cognitive,social, and economic, based on the information sources the techniques draw on in order topredict a user's reaction to an article. The three categories provide a useful road map tothe literature.

Cognitive, or content-based filtering techniques select documents based on the text inthem. For example, the kill files and string search features provided by news clientsperform content filtering. Even the division of netnews into newsgroups is a primitiveexample, since a reader restricts his attention to those articles with a particular textstring in their "newsgroup:" field.

Other content-based filtering techniques could potentially be used as well. The profileof which texts to include or kill could be more complex than a collection of characterstrings. For example, strings could be combined with the Boolean operators AND, OR, andNOT. Alternatively, the profile could consist of weight vectors, with the weightsexpressing the relative importance of each of a set of terms [4, 5, 16].

Some content filtering techniques update the profiles automatically based on feedbackabout whether the user likes the articles that the current profile selects. Informationretrieval research refers to this process as relevance feedback [17]. The techniques forupdating can draw on Bayesian probability [2], genetic algorithms [18], or other machinelearning techniques.

Social filtering techniques select articles based on relationships between people andon their subjective judgments. Placing an author's name in a kill file is a crude example.More sophisticated techniques might also filter out articles from people who previouslyco-authored papers with the objectionable person.

Collaborative filtering, based on the subjective evaluations of other readers, is aneven more promising form of social filtering. Human readers do not share computers'difficulties with synonymy, polysemy, and context when judging the relevance of text.Moreover, people can judge texts on other dimensions such as quality, authoritativeness,or respectfulness. A moderated newsgroup employs a primitive form of collaborativefiltering, choosing articles for all potential readers based on evaluations by a singleperson, the moderator.

The Tapestry system [6] makes more sophisticated use of subjective evaluations. Thoughit was not designed to work specifically with netnews, it allows filtering of all incominginformation streams, including netnews. Many people can post evaluations, not just asingle moderator, and readers can choose which evaluators to pay attention to. Theevaluations can contain text, not just binary accept/reject recommendations. Moreover,filters can combine content-based criteria and subjective evaluations. For example, areader could request articles containing the word "CSCW" that Joe has evaluatedand where the evaluation contains the word, "excellent".

Our work is similar in spirit to Tapestry but extends it in two ways. First, Tapestryis a monolithic system designed to share evaluations within a single site. We shareratings between sites and our architecture is open to the creation of new news clients andrating servers that would use the evaluations in different ways. Second, Tapestry does notinclude any aggregate queries. The rating servers we have implemented aggregate ratingsfrom several evaluators, based on correlation of their past ratings. A reader need notknow in advance whose evaluations to use and in fact need not even know whose evaluationsare actually used. In GroupLens, ratings entered under a pseudonym are just as useful asthose that are signed.

Maltz has developed a system that aggregates all ratings of each netnews article,determining a single score for each [14]. By contrast, GroupLens customizes scoreprediction to each user, thus accommodating differing interests and tastes. In return forits reduced functionality, Maltz's scheme scales better than ours, because rating serverscan exchange summaries of several users' ratings of an article, rather than individualratings.

The subjective evaluations used in collaborative filtering may be implicit rather thanexplicit. Read Wear and Edit Wear [7] guide users based on other users' interactions withan artifact. The GroupLens news clients monitor how long users spend reading each articlebut our rating servers do not yet use that information when predicting scores.

Economic filtering techniques select articles based on the costs and benefits ofproducing and reading them. For example, Malone argues that mass mailings have a lowproduction cost per addressee and should therefore be given lower priority. Applying thisidea to netnews, a news client might filter out articles that had been cross-posted toseveral newsgroups. More radical schemes could provide payments (in real money orreputation points) to readers to consider articles and payments to producers based on howmuch the readers liked the articles.

Stodolsky has proposed a scheme that combines social and economic filtering techniques[19]. He proposes on-line publications where the publication decision ultimately restswith the author. During a preliminary publication period, other readers may post ratingsof the article. The author may then withdraw the article, to avoid the cost to hisreputation of publishing an article that is disliked.


The GROUPLENS section of the paper describes the GroupLens architecture and itsevolution. The ONGOING EXPERIMENTATION section describes a larger scale test of thearchitecture that is in preparation. The SOCIAL IMPLICATIONS section addresses socialchanges in the use of Netnews that may be precipitated by GroupLens.


(Video) The Most Cited CSCW Paper Ever

GroupLens is a distributed system for gathering, disseminating, and using ratings fromsome users to predict other users' interest in articles. It includes news reading clientsfor both Macintosh and Unix computers, as well as "Better Bit Bureaus," serversthat gather ratings and make predictions. Both the overall architecture and particularcomponents have evolved through iterative design and pilot testing to meet the followinggoals:

Openness: There are currently dozens of news clients in common use, each with astrong following among its user community. Any or all of these clients can be adapted toparticipate in GroupLens. GroupLens also allows for the creation of alternative Better BitBureaus that use ratings in different ways to predict user interest in news articles.

Ease of Use: Ratings are easy to form and communicate, and predictions are easyto recognize and interpret. This minimizes the additional burden that collaborativefiltering places on users.

Compatibility: The architecture is compatible with existing news mechanisms.Compatibility reduces user overhead in taking advantage of the new tool, and simplifiesits introduction into netnews.

Scalability: As the number of users grows, the quality of predictions shouldimprove and the speed not deteriorate. One potential limit to growth will be transport andstorage of the ratings, if GroupLens grows very large.

Privacy: Some users would prefer not to have others know what kinds of articlesthey read and what kinds they like. The Better Bit Bureaus in GroupLens can make effectiveuse of ratings even if they are provided under a pseudonym.


Usenet consists of Internet sites as well as UUCP sites. Typically a site will declarea machine to act as its news server. Users at each site invoke news clients on theircomputers and connect to the news server in order to retrieve news articles. Users canalso write new articles and post them to the news server through their news clients.

When a user posts an article, it travels from the news client where the article iscomposed to the local news server and from there to news servers at nearby sites. Afterleaving the originating site, an article propagates throughout Usenet, hopping from siteto site. Since there is no centralized coordination of the distribution process, anarticle may arrive at a site via more than one route. Because articles have globallyunique identifiers, however, and are never altered once they are posted, any site canrecognize a duplicate copy of an article and avoid passing it on. Lotus Notes uses asimilar distribution process [10]. The netnews architecture is summarized in Figure 1.

GroupLens adds one new type of entity to the netnews architecture, Better Bit Bureaus,as shown in Figure 2. The Better Bit Bureaus provide scores that predict how much the userwill like articles, and gather ratings from news clients after the user reads thearticles. The Better Bit Bureaus also use special newsgroups to share ratings with eachother, to allow collaborative filtering among users at different sites. The remainder ofthis section traces the processes of rating creation, distribution, and use and describeshow they meet

GroupLens: An Open Architecture for Collaborative Filtering of Netnews (1)

Figure 1: The netnews architecture. News articles hop from news server to news server.A news client connects to the news server at its site and presents articles to users. GroupLens: An Open Architecture for Collaborative Filtering of Netnews (2)

Figure 2: The GroupLens architecture. Better Bit Bureaus collect ratings from clients,communicate them by way of news servers, and use them to generate numeric scorepredictions that they send to clients. Clients connect to a local news server, and canconnect to a Better Bit Bureau that uses the same or a different news server.the designgoals of openness, ease of use, compatibility, scalability, and privacy.

Entering Ratings

In GroupLens, a rating is a number from 1 to 5, optionally supplemented by the numberof seconds which the user spent reading the article. Users are encouraged to assignratings based on how much they liked the article, with 5 highest and 1 lowest. The userchooses a pseudonym to associate with her ratings that may be different from the name sheuses for posting news articles. This preserves the ability to detect that two ratings camefrom the same person, while preventing detection of exactly who that person is.

The GroupLens choice of the form and meaning of ratings is only one possibility in arich design space. There are many possible dimensions along which to rate articles:interest in subject, quality of writing, authoritativeness of the author, etc. Rather thana single composite rating, separate ratings on several dimensions could be solicited fromreaders. Free text ratings could be entered rather than numbers. Readers could be asked topredict how well they think other readers will like an article rather than report how muchthey themselves liked it. Ratings could be restricted only to positive, or only tonegative evaluations. The degree of privacy could also be varied, from completelyanonymous to authenticated signatures.

In fact, an earlier implementation of a Macintosh news client [20] employed ratingswith quite a different form than the current GroupLens architecture. Users entered onlyendorsements, positive ratings, on the assumption that since the signal to noise ratio innetnews is so low it is only important to point out the good articles. Readers endorsedarticles that they thought others in a known small group would like. Finally, readerssigned endorsements with their real names, allowing other people to select all thearticles endorsed by a particular friend.

A pilot test of that earlier endorsement mechanism at a Schlumberger research labindicated that a group of seven people may not be large enough to get the full availablebenefit of collaborative filtering. As we contemplated a much larger group size, webelieved that some users would be less willing to sign their ratings and that it wouldbecome increasingly difficult for users to know what articles others in the group wouldlike.

The pilot test also reinforced the importance of making it as easy as possible to enterendorsements. To make an endorsement, a user had to select from a pull-down menu, wait fora window to open up, optionally enter text in the window, and then close it. While thewhole process took only a matter of seconds if the user entered no text, it was stillsignificantly longer than it normally takes to go on to the next article.

We have taken care in the GroupLens system to make entry of ratings as easy aspossible. We have modified three news clients, Emacs Gnus and NN for UNIX machines andNewsWatcher for Macintoshes. In each case, entry of a

GroupLens: An Open Architecture for Collaborative Filtering of Netnews (3)

Figure 3. Reading an article with the modified NewsWatcher client. The user can clickon one of the five ratings buttons with the mouse, or type a number from 1 to 5 on thekeyboard.

rating fits into the overall paradigm of the news client. For example, in the modifiedNewsWatcher, the numbers 1 to 5 appear as selectable buttons any time a user reads anarticle (Figure 3), and the user can also type a number as a keyboard shortcut for thosebuttons. In Gnus, no buttons are displayed, but readers still type the ratings directly.With NN, readers first type the letter `v' (to enter into "rating mode") andthen the rating.

The GroupLens architecture requires only that ratings be reported on a 1 to 5 scale,not that they be displayed by news clients on that scale. To make the rating scale easyfor students to understand, the NN and Gnus clients accept letter grades rather thannumbers. When reporting the ratings to the Better Bit Bureau, they translate `a' to 5, `b'to 4 and so on. Other news clients could allow more gradations of ratings (e.g., 1 to 100)and report them as fractions between 1 and 5.

Distributing Ratings

GroupLens does not interfere with the Usenet propagation scheme at all. On thecontrary, it relies upon it heavily. The Better Bit Bureau packages one or more ratingsinto a news article, following the format in Figure 4, and posts it to a news server. Thisallows GroupLens to take advantage of the Usenet propagation scheme. Over the years Usenethas demonstrated its ability to propagate articles to every other Usenet site, even as thenumber of news servers has grown dramatically. Rating servers could exchange ratingsdirectly, through internet or UUCP links, but they would have to reimplement many of thepropagation features already found in Usenet.

The message format we have defined allows several ratings to be batched in a singlearticle. Each rating is just one line of text, while each Usenet netnews article requiresseveral lines of headers. Thus, packaging several ratings in one article can save aconsiderable amount of overhead. Our Better Bit Bureaus (BBBs) batch at the session level(i.e., all ratings entered by a user during a reading session go into one ratingsarticle). Other batching policies, such as all ratings from a site over the last hour,could be implemented.

Ratings are posted in newsgroups dedicated solely to ratings articles. One naturalconfiguration is to set up a parallel "ratings transport" newsgroup for each"normal" Usenet group. One deficiency of this approach is that if a ratingarticle contains several ratings, it may have to be cross-posted to many ratingsnewsgroups. Another deficiency is that it requires news servers to carry a large number ofnew newsgroups devoted solely to ratings, which may increase administrative overhead.Currently, our BBBs post all ratings in a single newsgroup.

To facilitate the initial spread of GroupLens, users can participate even if theirlocal news servers do not carry the ratings newsgroup and even if their local siteadministrators have not set up Better Bit Bureaus. The GroupLens architecture permits thisby allowing users to connect to a remote BBB. The left side of Figure 2 illustrates alocal BBB that posts ratings articles to the same news server that the clients connect to.The right side of Figure 2 illustrates a client connecting to a remote BBB that propagatesratings articles through a different news server.

Predicting Scores

The Better Bit Bureaus (BBBs) predict how much readers will like articles. Whilecontent filters would make predictions based on the presence or absence of words in thearticles, the BBBs in GroupLens use the opinions of other people who have already ratedthe articles. If no one has read an article, the BBBs are unable to make predictions aboutit.

When ratings for an article are available, they are unlikely to be uniform, due todifferences of opinion and goals among the raters. A BBB combines the different ratings toproduce a predicted score. Moreover, additional readers are likely to have differentopinions about the article. A BBB thus might use the same ratings to predict differentscores for different readers, by changing the relative weight given to the ratings.

When predictions are on the same scale as ratings, prediction can be modeled as matrixfilling, where the columns are people, the rows are articles, and the cells contain theratings that people have posted, as shown in Figure 5. Many of the cells of the matrix areempty, because readers have not yet examined those articles or have elected not to ratethem. A BBB predicts scores for missing cells before the readers examine the correspondingarticles.

From: MIT GroupLens Better Bit Bureau

(Video) Recommender Systems: Classification, Evaluation | Pedro Ramaciotti Morales

Subject: Ratings; please ignore

Message-ID: <>

Groups_Rated: news.adin.policy, news.groups

Raters: [Pseudo1]

<> [Pseudo1] 1 12 news.adin.policy

<> [Pseudo1] 2 7 news.groups

Figure 4: A sample ratings article. Each line in the body of the article contains arating of one article by one person. The five fields on each line are the id of thearticle, the pseudonym of the rater, a rating, the number of seconds the reader spentexamining the article before rating it, and the newsgroups the article is in. The timecount is optional. Additional keyword identified fields can also be included at the end ofline.

GroupLens: An Open Architecture for Collaborative Filtering of Netnews (4)

Figure 5: a sample matrix of ratings.

All the scoring methods we have implemented are based on the heuristic that people whoagreed in the past are likely to agree again, at least on articles in the same newsgroup.This heuristic will mislead on occasion, but preferences for most kinds of articles arelikely to be fairly stable over time.

To implement this heuristic, our BBBs first correlate ratings on previous articles todetermine weights to assign to each of the other people when making predictions for one ofthem. Then, they use the weights to combine the ratings that are available for the currentarticle. We have investigated several techniques for correlating past behavior and usingthe resultant weights, based on reinforcement learning [12], multivariate regression, andpairwise correlation coefficients that minimize linear error or squared error.

We illustrate one of the correlation and prediction techniques by computing Ken'spredicted score on article 6, the last row of the matrix. First, we compute correlationcoefficients [15], weights between -1 and 1 that indicate how much Ken tended to agreewith each of the others on those articles that they both rated. For example, Ken'scorrelation coefficient with Lee is computed as:

GroupLens: An Open Architecture for Collaborative Filtering of Netnews (5)

In the formula above, GroupLens: An Open Architecture for Collaborative Filtering of Netnews (6) is the average of Ken's ratings. Allthe summations and averages in the formula are computed only over those articles that Kenand Lee both rated. We have conveniently arranged for GroupLens: An Open Architecture for Collaborative Filtering of Netnews (7) and GroupLens: An Open Architecture for Collaborative Filtering of Netnews (8) to be 3 in this example, but that need not be true in practice.

Similarly, Ken's correlation coefficient with Meg is +1 and with Nan is 0. That is, Kentends to disagree with Lee ( GroupLens: An Open Architecture for Collaborative Filtering of Netnews (9)) and agree with Meg ( GroupLens: An Open Architecture for Collaborative Filtering of Netnews (10)). His ratings are not correlated with Nan's.

To predict Ken's score on the last article in the matrix, take a weighted average ofall the ratings on article 6 according to the following formula:

GroupLens: An Open Architecture for Collaborative Filtering of Netnews (11)

This is a reasonable prediction for Ken, since the article received a high rating fromsomeone who agreed with him in the past and a low rating from someone who disagreed.Carrying through similar calculations for Nan yields a lower prediction of 3.75. Since Nanhad partial agreement with Lee in the past, Lee's low rating for the article partiallycancels out the high ratings that Meg gave it.

The score prediction system is robust with respect to certain differences ofinterpretation of the rating scale. If two users are perfectly correlated, but one usergives only scores between 3 and 5 and the other only scores between 1 and 3, a 5 scorefrom the first user will result in a prediction of 3 for the second. If two users would beperfectly correlated, but the first mistakenly thinks 1 is a good score and 5 is bad, thetwo will be negatively correlated and a 1 score from the first will result in a predictionof 5 for the second. This leads to a clear explanation to the user of how to assignratings: assign the rating you wish GroupLens had predicted for this article.

Allen's study of five subjects' preferences for newswire articles [1] found very smallcorrelations between subjects, thus calling into question our basic assumption that peoplewho agreed in the past are likely to agree again. It may be, however, that a larger sampleof subjects would have yielded some pairs with larger overlaps in their ratings. Moreimportantly, it may be that pairs of people will share interests in some topics but notothers. Two people may agree in their evaluations of technical articles, but not jokes.Our BBBs keep separate rating matrices for each newsgroup.

One hopes that the accuracy of the predictions improve as the BBB has more past ratingsto use in computing correlations. Four people at the University of Minnesota participatedin a pilot test of an earlier version, using a slightly different scoring function. Whileall four participants reported that the predicted scores eventually matched theirinterests fairly closely, they did observe that there was a start-up interval before thepredictions were very useful. Further experiments and analysis are necessary to determinejust how long the start-up interval is likely to be for each new user.

It seems likely that better scoring mechanisms can be developed. In addition to bettermatrix filling techniques, it may be helpful to use both others' ratings and the contentsof articles in making predictions. It may also be helpful to take into account the timepeople spent reading articles before rating them, information collected but not used byour BBBs.

Fortunately, the GroupLens architecture is open: anyone can implement an alternativeBBB so long as it posts ratings articles in the format described above and communicateswith clients the same way that our BBBs do. We hope that the development of alternativeBBBs will become an active area for future research. As we describe below, our next pilottest should yield rating sets that we will make available to others who wish to evaluatealternative scoring algorithms.

Using Ratings

It is up to the news client how best to use the scores generated by a BBB. Some mayfilter out those articles with scores below a threshold. Some may sort the articles basedon the scores. Others may simply display the scores, numerically or graphically. Inkeeping with the ease of use design goal, developers should modify each news client in amanner consistent with that client's overall design.

One trend in news clients is to display a summary of the unread articles in anewsgroup. Each line of the summary contains information about one article, typically theauthor, the subject line and the length. A user browses the summary and requests displayof the full text of those articles that seem interesting. All three of the news clients wemodified use this display technique.

The three modified clients we implemented make slightly different uses of the scores inthe summary display. The modified NN client displays articles in the same order a regularNN client does, namely the order in which the articles arrived at the news server. Itmerely adds an additional column containing the predicted scores. In the first version ofthis client, the scores were displayed numerically.

The modified Gnus client uses the predicted scores to alter the order of presentationof articles in the summary. Gnus clusters articles by thread. The modified Gnus clientsorts the threads based on the maximum predicted score over the articles in the thread.Within each thread, however, articles are still displayed in chronological order, topreserve the flow of discussion. As in the modified NN, the scores are displayed in anadditional column in the summary.

The Minnesota pilot test included users of both the Gnus and NN clients. As expected,participants tended to believe that the sorting and display mechanisms of their own newsreader were best, but all were glad to see the score predictions incorporated into thatstandard format.

Several users, however, noticed that it was somewhat difficult to visually scan thepredictions to find the high ones. A revised version of the NN client (Figure 6) roundsoff to the nearest integer and reports that as a letter grade (A-E), a scale familiar tostudents at U.S. Universities.

The modified NewsWatcher client displays the predicted scores as bar graphs rather thannumbers (Figure 7), making it easier to visually scan for articles with high scores(longer bars). Otherwise, it follows the conventions of the original NewsWatcher client.Articles are grouped into threads and the summary display initially shows header linesonly for the first article in each thread. Users can twist down the triangle associatedwith a thread to see the header lines for the rest of the articles.

GroupLens: An Open Architecture for Collaborative Filtering of Netnews (12)

Figure 6: The modified NN client. The third column displays the number of lines in thearticle. The fourth column displays the score predictions as letter grades, translatedfrom the numeric predictions that the Better Bit Bureau makes (5=A, 4=B, etc.). When noone has evaluated an article, no prediction is made.

GroupLens: An Open Architecture for Collaborative Filtering of Netnews (13)

(Video) Alan Said: Recommender systems. A brief and biased overview

Figure 7: The modified NewsWatcher client displays predicted scores as bar graphs.Disclaimer: the scores were randomly generated for demonstration purposes. In practice, wewould expect articles by Pete Bergstrom (one of the authors of this paper) to have muchhigher predicted scores.

Scale Issues

Further research is needed to understand how performance will change as the scaleincreases. In the case of GroupLens, there are several relevant performance measures:prediction quality, user time, Better Bit Bureau compute time and disk storage, andnetwork traffic.

The first measure is the quality of score predictions. We expect prediction quality toincrease as the number of users increases, since more data will be available to theprediction algorithm.

Another measure is how long users have to wait to post ratings and receive predictions.In an earlier version of GroupLens, the functions of the BBB were incorporated in the newsclient itself. One major advantage of the separate BBB is that it can pre-fetch ratingsand pre-compute predictions rather than computing them when the user starts the newsclient. Thus, user time should remain roughly constant as GroupLens grows, even if ittakes more CPU time to compute scores.

For many possible prediction formulas CPU time will grow even faster than linearly withincreases in the number of users. To reduce CPU time, BBBs could use only a part of theratings matrix, trading off compute time against quality of predictions.

Even though each rating is short, each news article might be read and rated by manyraters, so the total volume of ratings could exceed the volume of news. To minimizestorage requirements, BBBs may employ algorithms that use and discard ratings as theyarrive, rather than storing them.

Three basic techniques could reduce network traffic: reduce the size of the ratings,reduce the number of ratings, and reduce the number of places where each rating is sent.Our BBBs batch several ratings in a single article, a first step toward reducing theamount of storage per rating, but further compression is possible. The number of ratingscould be reduced by limiting the total number of ratings per article or the number ofratings from users with similar profiles.

The separation of the BBBs from the news clients in the GroupLens architecture reducesthe number of destinations for each rating: each news client receives only scorepredictions rather than all the individual ratings that contribute to those predictions.

The number of destinations for each rating could be further reduced by sending ratingsto some BBBs but not others. For example, BBBs could be clustered, based on geography orinterest, and exchange ratings only within clusters. The size of each cluster must besmall enough to limit the amount of ratings information distributed, but large enough toprovide an effective peer group. The table below estimates daily network traffic forvarious cluster sizes assuming each user rates 100 articles per day and each ratingrequires approximately 100 bytes. For comparison purposes, the current netnews traffic isaround 100MB per day.

Cluster size Daily ratings traffic 100 users 1 MB 10,000 users 100 MB 1,000,000 users 10 GB 

Summary of GroupLens Architecture

The heart of GroupLens is an open architecture for distributing ratings. Thearchitecture specifies the format of ratings produced in batches by BBBs, the propagationof the ratings by Usenet, and the interface for delivering predictions and ratings betweennews clients and BBBs. Otherwise, the architecture is completely open. BBBs and newsclients can be freely substituted, providing an environment for experimentation inpredicting ratings and in user interfaces for collecting ratings and presentingpredictions.


Both of the previous pilot tests, at Schlumberger and the University of Minnesota,involved only local sharing of ratings. These tests led to improvements in both theoverall architecture and the user interfaces of news clients, as discussed already. Thenext step is a larger scale, distributed test, that we plan to carry out this summer. Wehave established a newsgroup on the news servers at MIT and Minnesota and two (slightlydifferent) Better Bit Bureaus that communicate ratings through that newsgroup.

The test is not designed to demonstrate that people prefer to read netnews with ourcollaborative filters than without them. We believe that such an evaluation should waitfor at least one more iterative design cycle. Rather, the goals are to identify anyunexpected scaling issues that may arise and to gather a data set that will be useful inevaluating alternative score prediction algorithms.

The primary benchmark of any algorithm's effectiveness will be its ability to predictvalues that have been deleted from a rating matrix. At first glance, it might seem thatany large set of ratings would be useful in creating such a benchmark. Upon closerinspection, however, complete ratings matrices are much more valuable than sparse ones.For example, suppose that users read and rate only a small number of articles, based onscore predictions they receive from BBB X. If users read different articles, thisgenerates a sparse matrix of ratings. Now suppose that we wish to compare X to analternative, Y, that predicts different scores for the users. We can compare Y's and X'spredictions on those articles that users read, but the sample is biased. Perhaps with Y'sscores, the users would have read other articles and liked them.

To allow unbiased comparisons, we are asking each of the participants in the next pilottest to read and rate all the articles in a training set. The training set will contain anumber of articles from each of the newsgroups that will be included in the test. Sinceusers will contribute ratings under a pseudonym, we will be able to share the ratings inthis training set with other researchers. In addition, we will retain the full texts ofthe articles in the training set. That will enable evaluation of BBBs that perform contentfiltering, or a combination of content filtering and collaborative filtering, as well asthose that use only other users' ratings.


Collaborative filtering may introduce many social changes in the already rapidlyevolving Netnews community. For example, the utility of moderated newsgroups may decline.New social patterns will have to develop to encourage socially beneficial behaviors, suchas reviewing articles that have already received a few low ratings. Finally, if GroupLensis effective at creating peer groups with shared interests, will those peer groups bepermeable or will the global village fracture into tribes?

Changes to Netnews Behaviors

GroupLens has the potential to change Netnews as we now know it. For one thing thequality of articles individual users choose to read should increase. More significantly,as more and more users rely on GroupLens the total number of low-quality articles onUsenet may decrease significantly. Since few people will read such articles, the incentiveto post them will decrease. GroupLens may also supplant or supplement other establishedNetnews behaviors.

Moderated Newsgroups

GroupLens may reduce the need for moderated newsgroups. The advantages of GroupLensover the existing approach are that "moderators" can be groups of people as wellas individuals, and that each user can rely on a different moderator rather than having asingle moderator for the entire group.

Some newsgroups might choose to use both a moderator and GroupLens. The moderator of anewsgroup will make the initial pass through the article submissions. Peer ratings wouldthen allow further filtering.

Newsgroup Splits

Currently, newsgroups start off with broad topics and split into narrower topics astraffic increases. For example, the newsgroup eventually split into thesubgroups australian, canadian, rugby, pro, college, fantasy, misc, and one for each teamin the NFL. These splits are a form of content filtering, initiated and managed by theusers.

GroupLens users may find that many such splits are less important, and in some casesundesirable. Over the course of time users will find themselves reading only the subset ofthe newsgroup they are most interested in, as they correlate with a peer group withsimilar interests. Splits of interest between groups of users will appear naturally, withno additional user or administrative effort. Allowing the splits to happen throughGroupLens rather than through explicit content filtering allows more cross-pollination ofgeneral interest articles. For instance, interesting articles posted by Bills fans aboutan upcoming football game against the Cowboys would also reach Cowboys fans withGroupLens, but would not if the articles were posted in the more specialized


Kill files are a content filtering mechanism implemented in some news clients. Manyusers who strongly dislike particular subjects or particular authors, however, do not usekill files because they find the mechanism complicated and cumbersome. GroupLens might bean easier means to the same end. A user's peer group will give such articles low ratings,so only a few users will have to read them.


Individuals put additional effort, albeit a modest amount, into providing ratingsthrough GroupLens. These ratings provide benefit to other users who can use them to selectinteresting articles. It's a two-way street: everyone can be both a producer and aconsumer of ratings.

When someone reads and rates an article, there is an incentive to provide honestratings, because dishonest ratings will cause the BBB to make poor future predictions forthat user. On the other hand, there is no incentive to rate articles at all. On thecontrary, there is an incentive to wait for others' ratings rather than read and rate anarticle oneself. A certain amount of altruism or guilt may cause most people to "dotheir share" of rating, but fewer than the socially optimal number of ratings arelikely to be produced.

The four-person Minnesota pilot test included a high-volume newsgroup, rec.arts.movies.The volume of articles was so high that each participant was unwilling to read aone-quarter share of the total daily volume. The newsgroup was quickly dropped from thetest. It may be that a larger user population would generate ratings even for ahigh-volume list such as rec.arts.movies, but it is harder to draw on a"do-your-share" mentality when collaborating with larger groups of people.

There are other, more subtle incentive problems that can arise as well. For example,there is an asymmetry between the effects of positive and negative ratings. If the firstfew readers rate an article too highly, others will read the article and give it lowerratings. On the other hand, if the first few ratings of an article are negative, otherswho would have rated it highly may never look at it because of the initial negativerating.

(Video) UIST+CSCW: A Celebration of Systems Research in Collaborative and Social Computing

To avoid this, it may be necessary to provide external incentives to some people toread and rate articles that have initially low ratings. The external incentives could bemoney, fame, or simply access to others' ratings: those who did not contribute their shareof ratings might be denied access to the Better Bit Bureau's predictions.

Global Villages

Present newsgroups, like newspapers and local television shows before them, provide ashared history for their community of readers. With GroupLens, users may choose to readarticles only from a small group with whom they share many common interests. Over timethis could lead to a fracture of the global village into many small tribes, each forming avirtual community but nonetheless isolated from each other.

Some kind of fracture is inevitable and even desirable, because no user can keep upwith the overwhelming volume of news produced each day. The question is whether thesubgroups will be closed or permeable. One argument for prognosticating permeability isthat many groups will form for a short time and then disband [3]. Another is that manyusers will participate in several subgroups, providing a mechanism for the best ideas tocross boundaries of interest groups.


Shared evaluations are useful in all sorts of activities. We ask friends, colleagues,and professional reviewers for their opinions about books, movies, journal articles, cars,schools, and neighborhoods. Clearly, some form of shared evaluations should also help infiltering electronic information streams such as netnews. It is not yet clear exactly whatform those evaluations should take, how they should be collected and disseminated, and howthey should be used in selecting articles to read.

GroupLens is one promising approach. A single number gives a composite rating of anarticle on all dimensions relevant to a particular reader. We have modified three newsreading clients to enable easy entry of such numeric ratings. We have also modified theway that the clients display subject lines to include predicted scores based on others'ratings.

Naturally, there will be differences of opinion among readers about particulararticles, due to varying interests or quality assessments. To accommodate differences ofopinion, not all readers will place equal trust in particular evaluators. The algorithmswe have implemented automatically determine how much weight to place on each evaluation,based on the degree of correlation between past opinions of the reader and evaluator. Thishas the beneficial side effects that readers need not know initially whose evaluations totrust and the evaluators' opinions can become trusted even if the evaluators choose toremain anonymous.

The GroupLens architecture allows new users to connect and new rating servers to comeon line, without global coordination. A new user need only use a modified news client andhave a connection to a rating server. The user need not convince the administrator of hernetnews server to modify the news server, run any additional software, or even to carryany additional newsgroups. A new rating server needs only to get access to a news serverthat carries the ratings newsgroups.

Moreover, the architecture is open. Anyone who wishes to can modify a news client toallow entry of evaluations or to use predicted scores, so long as the client follows theprotocol we have established for communicating with the rating server. Anyone who wishesto improve on the score predictions that our rating servers make can do so. There may bebetter ways to correlate past evaluations. There may also be ways to use the evaluationsin conjunction with content filtering. For example, when correlating past evaluations, thescoring algorithm might consider evaluations only of past articles that are somehowsimilar to the current one. Our next pilot test should yield a data set that can be usedfor evaluating alternative prediction methods.

Only further testing can reveal whether GroupLens gathers the right kind of evaluationsand uses them in ways that people like. If the simple numeric evaluations turn out to besufficient, the architecture will scale up to large numbers of rating servers and users.If not, then data from our tests will help develop and evaluate other mechanisms forsharing and using evaluations.

Right now, people read news articles and react to them, but those reactions are wasted.GroupLens is a first step toward mining this hidden resource.


Shumpei Kumon's keynote address at CSCW 92 [11] inspired our investigation of thepractical application of reputations to social filtering. Thanks to Lorin Hitt and CarlFeynman for helpful discussions about how to predict scores based on past correlations.Peter Foltz and Sue Dumais generously provided a test rating set generated from one oftheir experiments on content filtering [5]. Thanks also to Chris Avery, Joe Adler, YannisBakos, Erik Brynjolfsson, David Goldberg, Bill MacGregor, Tom Malone, David Maltz, VahidMashayekhi, Lisa Spears, Doug Terry, Mark Uhrmacher, and Zbigniew Wieckowski.


1. Allen, R.B. User Models: Theory, Method, and Practice. International Journal ofMan-Machine Studies, 32, (1990), pp. 511-543.

2. Belkin, N.J. and Croft, B.W. Information Filtering and Information Retrieval: TwoSides of the Same Coin? CACM, 35, 12 (1992), pp. 29-38.

3. Brothers, L., Hollan, J., Nielsen, J., Stornetta, S., Abney, S., Furnas, G. andLittman, M. Supporting Informal Communication via Ephemeral Interest Groups. In Proceedingsof CSCW 92 (1992, New York: ACM), pp. 84-90.

4. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R. Indexingby Latent Semantic Analysis. Journal of the American Society for Information Science,41, 6 (1990), pp. 391-407.

5. Foltz, P.W. and Dumais, S.T. Personalized Information Delivery: An Analysis ofInformation Filtering Methods. Communications of the ACM, 35, 12 (1992), pp. 51-60.

6. Goldberg, D., Nichols, D., Oki, B.M. and Terry, D. Using Collaborative Filtering toWeave an Information Tapestry. Communications of the ACM, 35, 12 (1992), pp. 61-70.

7. Hill, W.C., Hollan, J.D., Wroblewski, D. and McCandless, T. Edit Wear and Read Wear.In Proceedings of CHI 92 Conference on Human Factors in Computing Systems (1992,New York: ACM), pp. 3-9.

8. Kahn, R.E. and Cerf, V.G. The Digital Library Project, Volume 1: The Wold ofKnowbots. An Open Architecture for a Digital Library System and a Plan for Its Development. CNRI, 1895 Preston White Drive, Suite 100, Reston, VA 22091 Tech Report (March, 1988).

9. Karlgren, J. Newsgroup Clustering Based on User Behavior-- A RecommendationAlgebra . Swedish Institute of Computer Science #SICS-T--94/04-SE (March, 1994).

10. Kawell, L.J., Beckhardt, S., Halvorsen, T. and Ozzie, R. Replicated DocumentManagement in a Group Communication System. In Proceedings of CSCW 88 (1988, NewYork: ACM).

11. Kumon, S. From Wealth to Wisdom: A Change in the Social Paradigm. In Proceedingsof CSCW 92 (1992, New York: ACM), pp. 3.

12. Maes, P. and Kozierok, R. Learning Interface Agents. In Proceedings of AAAI 93 (1993,San Mateo, CA: American Association for Artifical Intelligence).

13. Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A. and Cohen, M.D. IntelligentInformation Sharing Systems. Communications of the ACM, 30, 5 (1987), pp. 390-402.

14. Maltz, D.A. Distributing Information for Collaborative Filtering on Usenet NetNews . MIT Department of EECS MS Thesis (May, 1994).

15. Pindyck, R.S. and Rubinfeld, D.L. Econometric Models and Economic Forecasts.MacGraw-Hill, New York, 1991.

16. Salton, G. and Buckley, C. Term-Weighting Approaches in Automatic Text Retrieval.Information Processing and Management, 24, 5 (1988), pp. 513-523.

17. Salton, G. and Buckley, C. Improving Retrieval Performance by Relevance Feedback.Journal of the American Society for Information Science, 41, 4 (1990), pp. 288-297.

18. Sheth, B. A Learning Approach to Personalized Information Filtering . MITDepartment of EECS MS Thesis (February, 1994).

19. Stodolsky, D.S. Invitational Journals Based Upon Peer Consensus . RoskildeUniversity Centre, Institute of Geography, Socioeconomic Analysis, and Computer Science.ISSN 0109-9779-29 #No. 29/ 1990 (, 1990).

(Video) LA Ruby Conference 2013 People who liked this talk also liked ... Building Recommendation...

20. Suchak, M.A. GoodNews: A Collaborative Filter for Network News . MITDepartment of EECS MS Thesis (February, 1994).

21. Wiederhold, G. Mediators in the Architecture of Future Information Systems. IEEEComputer, March, (1992), pp. 38-49.


1. Recommender System - Beyond Matrix Completion
(Shuyao Chen)
2. 数据挖掘 8 5 告诉你一个真实的推荐
3. CSCW 2020 Lasting Impact Award
4. Stanford Seminar - Nudging People Toward Exposure to Politically Diverse News Sources
(Stanford Online)
5. John T. Riedl
(Speaking Videos)
6. #DataScience Владислав Грозин. Тренды в рекомендательных системах
(CodeFest Russia)
Top Articles
Latest Posts
Article information

Author: Reed Wilderman

Last Updated: 04/23/2023

Views: 5593

Rating: 4.1 / 5 (52 voted)

Reviews: 83% of readers found this page helpful

Author information

Name: Reed Wilderman

Birthday: 1992-06-14

Address: 998 Estell Village, Lake Oscarberg, SD 48713-6877

Phone: +21813267449721

Job: Technology Engineer

Hobby: Swimming, Do it yourself, Beekeeping, Lapidary, Cosplaying, Hiking, Graffiti

Introduction: My name is Reed Wilderman, I am a faithful, bright, lucky, adventurous, lively, rich, vast person who loves writing and wants to share my knowledge and understanding with you.