Interacting with recommender systems

ABSTRACT

Many people today live in information-rich worlds, constantly facing the question: what should I do next? Which papers should I read to learn about a new area I am interested in? Which movie should I go to? Which restaurant would I like? The experience of friends and colleagues is a valuable resource for making such decisions, especially friends who are familiar with the subject area and have similar tastes.The field of recommender systems (or collaborative filtering) attempts to automate this process, e.g., by supporting people in making recommendations, finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task.

REFERENCES

• 1: Konstan J. A., Riedl, J., Borchers, A. Herlocker, J. L. Recommender Systems: A GroupLens Perspective, AAAI Workshop Recommender Systems 98, Papers from the 1998 Workshop Technical report WS-98-08, AAAI Press, Menlo Park California, 1998
• 2: The March 1997 issue of Communications of the ACM, edited by Hal Varian and Paul Resnick.
• 3: The Collaborative Filtering Resource web page, at http://www.sims.berkeley.edu/resources/collab

INDEX TERMS

Keywords:
collaborative filtering, implicit feedback, recommender system, social filtering, user interface