• Corpus ID: 8987583

Implicit Feedback for Recommender Systems

@inproceedings{Oard1998ImplicitFF,
  title={Implicit Feedback for Recommender Systems},
  author={Douglas W. Oard and Jinmook Kim},
  year={1998}
}
Can implicit feedback substitute for explicit ratings in recommender systems? If so, we could avoid the difficulties associated with gathering explicit ratings from users. How, then, can we capture useful information unobtrusively, and how might we use that information to make recommendations? In this paper we identify three types of implicit feedback and suggest two strategies for using implicit feedback to make recommendations. 

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References

SHOWING 1-10 OF 14 REFERENCES
Newsgroup Clustering Based On User Behavior - A Recommendation Algebra
TLDR
A scheme for users to peek at other users'' user models to extract information is proposed, in an information retrieval or information filtering domain.
Siteseer: personalized navigation for the Web
TLDR
Siteseer learns each user’s preferences and the categories through which they view the world, and at the same time it learns for each Web page how different communities or affinitybased clusters of users regard it.
GroupLens: applying collaborative filtering to Usenet news
TLDR
The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high.
Information filtering based on user behavior analysis and best match text retrieval
TLDR
This work proposes a technique that uses user behavior monitoring to transparently capture the user’s interest in information, and a technique to use this interest to filter incoming information in a very efficient way.
Knowledge-based assistance for accessing large, poorly structured information spaces
TLDR
A conceptual framework is developed that shows the need for a flexible organization of information access interfaces, personalized structure to deal with vocabulary mismatches and individual information needs, and semi-autonomous agents that assist in creating this personalized structure and an operational system (scINFOSCOPE) instantiate this framework allowing for the exploration and evaluation of the approach in realistic working environments.
The Anatomy of a Large-Scale Hypertextual Web Search Engine
Using collaborative filtering to weave an information tapestry
TLDR
Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser.
Citation indexing: its theory and application in science
Citation indexing-its theory and application in science, technology, and humanities , Citation indexing-its theory and application in science, technology, and humanities , مرکز فناوری اطلاعات و اطلاع
Read Wear and Edit Wear
  • In: Proceedings of ACM Conference on Human Factors in Computing Systems, CHI ’92: 3-9.
  • 1992
Implicit Ratings and Riltering
  • Proceedings of the 5 DELOS Workshop on Filtering and Collaborative Filtering, 10-12. Budapaest, Hungary, ERCIM.
  • 1997
...
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