Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/84847
Title: | A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems | Authors: | Guo, Guibing Zhang, Jie Yorke-Smith, Neil |
Keywords: | Bayesian similarity Recommender systems |
Issue Date: | 2016 | Source: | Guo, G., Zhang, J., & Yorke-Smith, N. (2016). A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems. ACM Transactions on the Web, 10(2), 8-. | Series/Report no.: | ACM Transactions on the Web | Abstract: | User-based collaborative filtering, a widely used nearest neighbour-based recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient. However, both of these measures consider only the direction of rating vectors, and suffer from a range of drawbacks. To overcome these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. We posit that not all the rating pairs should be equally counted in order to accurately model user correlation. Three different evidence factors are designed to compute the weights of rating pairs. Further, our principled method reduces correlation due to chance and potential system bias. Experimental results on six real-world datasets show that our method achieves superior accuracy in comparison with counterparts. | URI: | https://hdl.handle.net/10356/84847 http://hdl.handle.net/10220/41981 |
ISSN: | 1559-1131 | DOI: | 10.1145/2856037 | Schools: | School of Computer Science and Engineering | Rights: | © 2016 Association for Computing Machinery (ACM). This is the author created version of a work that has been peer reviewed and accepted for publication by ACM Transactions on the Web, Association for Computing Machinery (ACM). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2856037]. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
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