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| Content Provider | ACM Digital Library |
|---|---|
| Author | Yuan, Fajie Jose, Joemon M. Zhang, Weinan Guo, Guibing Yu, Haitao Chen, Long |
| Abstract | State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures. |
| Starting Page | 227 |
| Ending Page | 236 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450340731 |
| DOI | 10.1145/2983323.2983758 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Context-aware Lambdafm Top-n recommendation Factorization machines Pairwise ranking Prfm |
| Content Type | Text |
| Resource Type | Article |
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