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| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Local Collaborative Ranking | |||||||||||
| 文献作者 | Joonseok Lee; Samy Bengio; Seungyeon Kim | ||||||||||
| 文献发表年限 | 2014 | ||||||||||
| 文献关键字 | |||||||||||
| recommender systems; collaborative filtering; ranking | |||||||||||
| 摘要描述 | |||||||||||
| Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low- rank. In this paper, we examine an alternative approachin which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank approximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems. | |||||||||||
