WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation
WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation
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DOI：
10.1145/2766462.2767718
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发表时间：
2015-08
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影响因子：
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通讯作者：
Chao Chen;Dongsheng Li;Yingying Zhao;Q. Lv;L. Shang
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文献类型：
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作者：
Chao Chen;Dongsheng Li;Yingying Zhao;Q. Lv;L. Shang
矩阵近似是基于协作过滤的推荐系统的最有效方法之一。但是，大数据集上矩阵分解的高计算复杂性限制了其可扩展性。先前的解决方案采用了共聚类方法，将大矩阵划分为一组较小的子膜片，然后可以并行处理以提高可伸缩性。缺点是建议精度较低，因为子膜仅包含用户评级信息的子集。本文介绍了Wemarec，这是一种加权和集合矩阵近似方法，以进行准确且可扩展的建议。它建立在这样的直觉上：包含更频繁的某些用户/项目/评级样本的矩阵倾向于对这些特定的用户/项目/评级进行更可靠的评分预测。 Wemarec由两个重要组成部分组成：（1）根据每个子序列中的评分分布进行计算的加权策略，并应用于包含这些子膜片的单个矩阵； （2）一种合奏策略，该策略利用用户特定和特定于项目的评级分布来结合多组共聚类结果的近似矩阵。使用现实世界数据集的评估表明，Wemarec以建议精度优于最先进的矩阵近似方法（Movielens数据集的0.5？11.9％，Netflix数据集的2.2---13.1％，在3---11 x上提高了2.2---13.1％可伸缩性。
Matrix approximation is one of the most effective methods for collaborative filtering-based recommender systems. However, the high computation complexity of matrix factorization on large datasets limits its scalability. Prior solutions have adopted co-clustering methods to partition a large matrix into a set of smaller submatrices, which can then be processed in parallel to improve scalability. The drawback is that the recommendation accuracy is lower as the submatrices only contain subsets of the user-item rating information. This paper presents WEMAREC, a weighted and ensemble matrix approximation method for accurate and scalable recommendation. It builds upon the intuition that (sub)matrices containing more frequent samples of certain user/item/rating tend to make more reliable rating predictions for these specific user/item/rating. WEMAREC consists of two important components: (1) a weighting strategy that is computed based on the rating distribution in each submatrix and applied to approximate a single matrix containing those submatrices; and (2) an ensemble strategy that leverages user-specific and item-specific rating distributions to combine the approximation matrices of multiple sets of co-clustering results. Evaluations using real-world datasets demonstrate that WEMAREC outperforms state-of-the-art matrix approximation methods in recommendation accuracy (0.5?11.9% on the MovieLens dataset and 2.2--13.1% on the Netflix dataset) with 3--10X improvement on scalability.