Grouplens: Applying Collaborative Filtering to Usenet News. Joseph A. Konstan, Bradley N. Miller, Dave Maltz, Jonathan L. Herlocker, Lee R. Applying. Collaborative Filtering to Usenet News. THE GROUPLENS PROJECT DESIGNED, IMPLEMENTED, AND EVALUATED a collaborative filtering system. GroupLens: applying collaborative filtering to Usenet news. Jonatan Shinoda. Author. Jonatan Shinoda. Recommender Systems Recom Recommender Joseph .

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Skip to main content. Log In Sign Up. Miller, David Maltz, Collaboratie L. Collaborative Filtering to Usenet News High volume and personal taste makes Usenet news an ideal candidate for collaborative filtering techniques. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in collxborative the newsgroup organization allows readers to select the content that most interests them, in practice most newsgroups carry a wide enough spread of giltering and advanced language features may be useless to the to make most individuals consider Usenet news to be novice.

Furthermore, each Gtouplens combination of high volume and personal user values a different set of messages. Both taste and taste made Usenet news a promising candidate for prior knowledge are major factors in evaluating news collaborative filtering. More formally, we determined articles. For example, readers of the rec. The GroupLens project started in and ous postings, value articles based on whether they completed a pilot study at two sites to establish the perceive them to be funny.

Readers of technical feasibility of using collaborative filtering for Usenet groups, such as comp. Several critical design decisions were made on interest appling usefulness to them—introductory filterint part of that pilot study, including: Over a seven-week trial starting programs. Different domains have collabrative values for correct and incorrect predictions.

Missing a desirable legal citation can be then, the project has continued forward to undertake extremely costly, while missing a good movie is not since there the challenge of applying collaborative filtering to a are many desirable movies.

Similarly, the cost of mistakenly pick- larger set of users and on a larger scale. Moreover, we ing an undesirable restaurant is higher than the cost of picking an have focused our efforts on overcoming some of the undesirable science article due to the time and money invested. Newsgroups known to us only by pseudonyms. In [5] we present a more Typical users read only a tiny fraction of Usenet detailed summary of the trial results, along with news articles.

Assessing Predictive Utility Predictive utility refers generally to the value of hav- This article discusses the challenges involved in ing predictions for an item before deciding whether creating a collaborative filtering system for Usenet to invest time or money in consuming that item.

The public trial of GroupLens invited users Usenet, the items are news articles, but the concept from over a dozen newsgroups selected to represent a is general enough to include physical items such as cross-section of Usenet listed in Table 1 to apply our books or videotapes as well as other information news reader software to enter ratings and receive pre- items.

Humor how effectively predic- Percent articles Percent articles tions influence user con- 0. A 0,4 0,4 domain with high pre- dictive utility is one 0.

A domain with Comp. The desirability of an Figure 2. Ratings profiles for four Usenet news groups The percentage of item is a measure of a par- articles assigned each rating varies significantly from newsgroup to newsgroup. Items ratings in comp.

Figure 1 shows four too of occurrences number of occurrences 25 50 cost-benefit analyses.

The cost of Figure 3. User pair correlations for three newsgroups. One way to compare the similarity false positives is the price of users is to compute the Pearson coefficient between their ratings. Here, the number of user pairs with of the ticket plus the each Pearson coefficient is plotted for three different newsgroups. The presence of many high amount of time before the qpplying in the rec.

GroupLens: applying collaborative filtering to Usenet news | Jonatan Shinoda –

In the moderated newsgroup rec. Restaurant selec- GroupLens Xrn Client Server tion follows a similar pattern reader Library though the risk of going to an Generate undesirable restaurant is NNTP Predictions higher since you typically still Server have the filterring and the bill.


Process Ratings Legal research is very different. Udenet news Client reader Library Database The cost of missing a relevant and important precedent is very high, and may outweigh the cost of sifting through all Figure 4. Usenet clients connect to the GroupLens server of the potentially relevant cases through the GroupLens client library, and to a separate NNTP server as usual.

Because of the high The costs of misses and false positives represent volume of news, the value of correct rejections is high the risk involved in making a prediction. The values in many groups it is infeasible to read the entire of hits and correct rejection represent the potential group.

At the same time, the fact that so many users benefit of making predictions. Predictive utility is the read Usenet articles implies the value of a hit is also difference between the potential benefit and the risk. Thus, Usenet has a high potential Thus, the risk of mistakes is lowest for movies or sci- benefit.

It also has low risk. False positives are cer- entific articles, and the potential benefit is highest tainly annoying, but it takes only a few seconds for a for movies, articles, and restaurants.

And misses turn One important component of the cost-benefit out to be low cost as well since truly valuable articles analysis is the total number collaborativd desirable and undesir- tend to reappear in follow-up discussion, reducing the able items.

Of implies that any accurate prediction system will add course when there are many desirable items, users significant value—why then do we need a personal- may refine their desires to select only the most inter- ized collaborative filtering system?

Would it not be esting of the interesting ones given their limited easier to simply calculate average ratings across all time. We have found that per- value because the aggregate value of correct rejections sonalized predictions are significantly more accurate becomes high requiring a very high miss cost before than nonpersonalized averages. In general, users do it becomes preferable to predict cpllaborative all items are not agree on which articles are desirable. The group Usenet news is a domain with extremely high pre- rec.

While statistics vary by newsgroup, we due to a large number of cross-posted articles that do not even attempt to be funny, but there are a substan- 1Our analysis includes the effect of frequency of occurrence in the cost or tial number of low and negative user-pair correlations. Hence, correct rejections are worth more when there are many un- desirable items.

If we isolate frequency of occurrence, then the benefit of Rec. We find the filteirng analysis more intuitive, though relations that we believe represent people with over- separating the frequency from the per-item cost can be useful for some analyses. Hence, with millions of users and hundreds of software com- it is better not to lump all votes together since there ponents already written. In filterring ways, building col- are systematic differences in taste.

Moreover, even in laborative filtering into an existing domain provided an area where users agree overall, such as rec.

GroupLens: Applying Collaborative Filtering to Usenet News

We already knew the Table 2 shows that correlation between ratings and information resource was useful, as attested to by the predictions is dramatically higher for personalized millions of filteging already reading Usenet news.

We also predictions than for all-user average ratings. We GroupLens Architecture Overview already had a natural partitioning of content into hier- The GroupLens system architecture is designed to archical newsgroups that evolved through a democra- blend into the existing Usenet client-server architec- tic voting process and were likely to represent real ture.

At a high level, Figure 4 shows that a news clusters of content and interest. The NNTP server raised research problems. Correlations between ratings and predictions The problem of integrating interface to the server. The for average and personalized predictions with the sheer volume and typical usage pattern is for a diversity of news readers led us news reader to request a set of headers for unread toward the grouplena library and an open architecture articles from the NNTP server and pass the article model [6].


A quick survey showed over a dozen widely identifiers to the GroupLens client library to jsenet used news readers, and typically several versions of predictions.

As the user reads articles in the news- each in active use. These news readers ranged from group, the news reader records ratings with the nnn mmmmmm ooooo mmmmmmm mmm nnnnnnn mmmm nnnnnn mmmmmmm nnnnnnn nnnnn client library which File Edit Apps Options Buffers Tools Article Threads Misc Post Score Mascrypt Help sends them back to the server. Popeye’s Famous Fried Chicken Newsgroups: The Gnus 1 tsp cayenne pepper 2 tsp paprika interface with GroupLens 3 eggs predictions are shown here.

In a large, shallow bowl, combine the flour, salt, pepper, and paprika.

GroupLens: Applying Collaborative Filtering to Usenet News – Semantic Scholar

Break the eggs into a separate collabortive bowl and beat edge of the summary part until blended. Check the oil by dropping in a pinch of the flour mixture. If the oil bubbles rapidly around the flour, it is ready. Dip each piece of the interface. The longer of chicken into the eggs, then coat generously with the flour mixture.

Drop each piece into the hot oil and fry usenst 15 to 25 minutes, or until bars indicate articles that it is a dark golden brown. Remove the chicken to paper towels or a rack to drain. It is not clear what pre- dows, and Unix platforms. Since there was Rating entered by user lenge of effectively no standard protocol for integrating predic- exchanging ratings and Figure 6. Correlation between time spent reading and explicit tions into different predictions, we defined an ratings.

Readers who spend a long time with an article are more presentation models open protocol for commu- likely to rate it highly. Users typically Lens server. To further want to read news in simplify the task of caching data and following the roughly chronological order, grouped by discussion protocol, we implemented and distributed client thread.

When predictions are provided, users add the libraries written in C and in Perl. We found that a new interface com- ratings. We consider the client popular in the rec. Group- The diversity and sheer number of installed news Lens support is provided or forthcoming in Gnus 5. One of our test users in Poland approach. With this approach the implementers of wrote a proxy GroupLens server to download ratings each news reader could easily add access to the Group- and predictions each evening to help him deal with Lens server and could also use the returned predictions network throughput as low as 10bps.

This type of in whatever manner they found to be most consistent user participation giltering only come about with an open with their news reader interface.

The problem of integrating predictions into differ- A Dynamic and Fast-Paced ent presentation models was more formidable. The Information System original GroupLens system was designed for news Item volume and lifetimes are another way in which readers in which the user selected a newsgroup and Usenet news differs from other domains where col- was then given a split screen with one part containing laborative filtering has been applied.

Across all a list of unread articles in either chronological or dis- newsgroups, users will see 50, tonew cussion-thread order and the other part showing the messages each day, and the volume of postings is text of the currently selected article.

In this presenta- doubling each year. The useful lifetime of a Usenet tion model, it is simple and effective to display pre- message is short; most sites expire messages after dictions along with other header information to help approximately one week. Furthermore, there is no users choose which articles to read and which to skip. Usenet is a truly distributed system shown in Figure 5.