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CSREAEEE
2006

Using Temporal Information in Collaborative Filtering: An Empirical Study

10 years 4 months ago
Using Temporal Information in Collaborative Filtering: An Empirical Study
- Collaborative filtering is a widely used and proven method of building recommender systems that provide personalized recommendations on products or services based on explicit ratings from users. Recommendation accuracy becomes an important factor in some e-commerce environments (such as a mobile environment as a result of limited connection time and device size). As user preferences change over time, temporal information can improve recommendation accuracy. In this paper, we present a variety of temporal information and investigate how such temporal information affects the accuracy of collaborative filtering-based recommender systems. The temporal information includes item launch time, user buying time, the time difference between the two, as well as several combinations of these three types of temporal information. We conducted several experiments on a collaborative filtering system for recommending character images (wallpapers) in a mobile e-commerce environment. Empirical results ...
Young Park, Tong-Queue Lee
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where CSREAEEE
Authors Young Park, Tong-Queue Lee
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