WPI Computer Science Department

Computer Science Department

Data Recency in Collaborative Filtering Systems

Researchers: Yoonsoo Kim and Dave Brown.


Recommender systems are being widely applied in many E-commerce sites to suggest products, services, and information items to potential users. Collabora-tive filtering systems, the most successful recommender system technology to date, help people make choices based on the opinions of other people. While collaborative filtering systems have been a substantial success, there are several problems that researchers and commercial applications have identified: the early rater problem, the sparsity problem, and the large scale problem. Moreover, existing collaborative filtering systems do not consider data recency. For this reason, if a user's preferences have changed over time, the sys-tems might not recognize it quickly. This thesis studies how to apply data recency to collaborative filtering systems to get more predictive accuracy. We define the data recency problem as the negative impact of old data on the pre-dictive accuracy of collaborative filtering systems. In order to mitigate this shortcoming, the combinations of time-based forgetting mechanisms, pruning and non-pruning strategies and linear and kernel functions, are utilized to ap-ply weights. A clustering technique is employed to detect the user's changing preferences. We apply our research approach to the DeliBook dataset. The goal of our experiments is to show that our algorithm that incorporates temporal factors provides better recommendations than existing methods.


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dcb @ cs.wpi.edu 30 Sep 2004