GroupLens for Usenet: Experiences in Applying Collaborative Filtering to a Social Information System

Miller, Bradley N.; Ried, John T.; Konstan, Joseph A.

doi:10.1007/978-1-4471-0057-7_10

Cited by 18 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The shape of this distribution is similar to that observed in other studies of display time [12,13,15]. To adjust this distribution, a logarithm transformation was performed using the natural log.…”

Section: Figure 2 Origin Of Display Timementioning

confidence: 88%

“…Recent research has had some success using information-seeking behaviors as implicit feedback for document preference [4,5,10,12,13,15,20]. See [4,9,14] for a review and classification of this research.…”

Section: Introductionmentioning

confidence: 99%

“…Most studies have only investigated a single task, such as news or job searching. For instance, Morita and Shinoda [13] and Miller, et al [12] considered the behavior of users interacting with online news services like Netnews and Usenet. Rafter and Smyth [15] considered the behavior of users as they interacted with an online job bank.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Display time as implicit feedback

Kelly

Belkin

2004

Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

179

109

View full text Add to dashboard Cite

Recent research has had some success using the length of time a user displays a document in their web browser as implicit feedback for document preference. However, most studies have been confined to specific search domains, such as news, and have not considered the effects of task on display time, and the potential impact of this relationship on the effectiveness of display time as implicit feedback. We describe the results of an intensive naturalistic study of the online information-seeking behaviors of seven subjects during a fourteen-week period. Throughout the study, subjects' online information-seeking activities were monitored with various pieces of logging and evaluation software. Subjects were asked to identify the tasks with which they were working, classify the documents that they viewed according to these tasks, and evaluate the usefulness of the documents. Results of a user-centered analysis demonstrate no general, direct relationship between display time and usefulness, and that display times differ significantly according to specific task, and according to specific user.

show abstract

Section: Figure 2 Origin Of Display Timementioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Display time as implicit feedback

Kelly

Belkin

2004

Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

179

109

View full text Add to dashboard Cite

show abstract

“…Schafer [19] examines and categorizes a large set of these commercialized recommender systems. In addition, numerous recommenders in a variety of domains have been developed for research purposes, including GroupLens (Usenet news) [11], Ringo (music) [20], and Jester (jokes) [7].…”

Section: Recommender Systemsmentioning

confidence: 99%

Shilling recommender systems for fun and profit

Lam

Riedl

2004

Proceedings of the 13th International Conference on World Wide Web

465

320

View full text Add to dashboard Cite

Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.

show abstract

“…For this, they also looked at a sequence of characteristic user behavior patterns. Some earlier work also utilizes dwell distributions for collaborative filtering to predict user ratings [34]. All of that work was done at the document level, whereas we focus here on section-level dwell time.…”

Section: Related Workmentioning

confidence: 99%

Improving passage ranking with user behavior information

Kong

Aktolga

Allan

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

User behavior information has proved valuable for inferring document relevance, but its role in deducing relevance at the passage/section level is not well explored. In this paper, we study how user behavior information implies section relevance, and use this information to improve section ranking. More specifically, we focus on four types of user search behavior that occur while browsing a document -dwell time, highlighting, copying and clicks at the section level. Experimental results based on a commercial query log show that user behavior information can significantly improve section ranking. While section-level click information is a very powerful signal of relevance, it depends on an interface supporting section-level links. We find comparable levels of gain using other behavior information that does not depend upon such an interface.

show abstract

GroupLens for Usenet: Experiences in Applying Collaborative Filtering to a Social Information System

Cited by 18 publications

References 18 publications

Display time as implicit feedback

Display time as implicit feedback

Shilling recommender systems for fun and profit

Improving passage ranking with user behavior information

Contact Info

Product

Resources

About