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SIGIR
2004
ACM

An automatic weighting scheme for collaborative filtering

9 years 2 months ago
An automatic weighting scheme for collaborative filtering
Collaborative filtering identifies information interest of a particular user based on the information provided by other similar users. The memory-based approaches for collaborative filtering (e.g., Pearson correlation coefficient approach) identify the similarity between two users by comparing their ratings on a set of items. In these approaches, different items are weighted either equally or by some predefined functions. The impact of rating discrepancies among different users has not been taken into consideration. For example, an item that is highly favored by most users should have a smaller impact on the user-similarity than an item for which different types of users tend to give different ratings. Even though simple weighting methods such as variance weighting try to address this problem, empirical studies have shown that they are ineffective in improving the performance of collaborative filtering. In this paper, we present an optimization algorithm to automatically compute the w...
Rong Jin, Joyce Y. Chai, Luo Si
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where SIGIR
Authors Rong Jin, Joyce Y. Chai, Luo Si
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