Distributed collaborative filtering with singular ratings for large scale recommendation.
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| Title: | Distributed collaborative filtering with singular ratings for large scale recommendation. |
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| Authors: | Xu, Ruzhi1,2 xrzpuma@gmail.com, Wang, Shuaiqiang1,2 shqiang.wang@gmail.com, Zheng, Xuwei2 xuwei_zheng@gmail.com, Chen, Yinong3 yinong@asu.edu |
| Source: | Journal of Systems & Software. Sep2014, Vol. 95, p231-241. 11p. |
| Subject Terms: | *DISTRIBUTED computing, *RECOMMENDER systems, *INFORMATION theory, MATHEMATICAL singularities, LARGE scale systems, COMPUTER users |
| Abstract: | Collaborative filtering (CF) is an effective technique addressing the information overloading problem, where each user is associated with a set of rating scores on a set of items. For a chosen target user, conventional CF algorithms measure similarity between this user and other users by utilizing pairs of rating scores on common rated items, but discarding scores rated by one of them only. We call these comparative scores as dual ratings, while the non-comparative scores as singular ratings. Our experiments show that only about 10% ratings are dual ones that can be used for similarity evaluation, while the other 90% are singular ones. In this paper, we propose SingCF approach, which attempts to incorporate multiple singular ratings, in addition to dual ratings, to implement collaborative filtering, aiming at improving the recommendation accuracy. We first estimate the unrated scores for singular ratings and transform them into dual ones. Then we perform a CF process to discover neighborhood users and make predictions for each target user. Furthermore, we provide a MapReduce-based distributed framework on Hadoop for significant improvement in efficiency. Experiments in comparison with the state-of-the-art methods demonstrate the performance gains of our approaches. [ABSTRACT FROM AUTHOR] |
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| Database: | Business Source Index |
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