Time weight collaborative filtering

Abstract

Collaborative filtering is regarded as one of the most promising recommendation algorithms. The item-based approaches for collaborative filtering identify the similarity between two items by comparing users' ratings on them. In these approaches, ratings produced at different times are weighted equally. That is to say, changes in user purchase interest are not taken into consideration. For example, an item that was rated recently by a user should have a bigger impact on the prediction of future user behaviour than an item that was rated a long time ago. In this paper, we present a novel algorithm to compute the time weights for different items in a manner that will assign a decreasing weight to old data. More specifically, the users' purchase habits vary. Even the same user has quite different attitudes towards different items. Our proposed algorithm uses clustering to discriminate between different kinds of items. To each item cluster, we trace each user's purchase interest change and introduce a personalized decay factor according to the user own purchase behaviour. Empirical studies have shown that our new algorithm substantially improves the precision of item-based collaborative filtering without introducing higher order computational complexity.

DOI: 10.1145/1099554.1099689

Extracted Key Phrases

10 Figures and Tables

02040'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

264 Citations

Semantic Scholar estimates that this publication has 264 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Ding2005TimeWC, title={Time weight collaborative filtering}, author={Yi Ding and Xue Li}, booktitle={CIKM}, year={2005} }