Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
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DOI：
10.1145/2959100.2959182
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发表时间：
2016-09
期刊：
影响因子：
--
通讯作者：
Dawen Liang;Jaan Altosaar;Laurent Charlin;D. Blei
中科院分区：
文献类型：
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作者：
Dawen Liang;Jaan Altosaar;Laurent Charlin;D. Blei
矩阵分解模型及其扩展是现代推荐系统中的标准模型。MF模型将观察到的用户-项目交互矩阵分解为用户和项目潜在因素。本文提出了一种协同分解模型--余因模型，该模型联合分解具有共享项目潜在因素的用户-项目交互矩阵和项目-项目共现矩阵。对于每一对物品，共现矩阵编码已经消费了两个物品的用户数量。余因受到最近单词嵌入模型(例如，word2vec)的成功的启发，该模型可以被解释为对单词共现矩阵进行因式分解。实验结果表明，在不增加额外计算开销的情况下，该模型在多个数据集上的性能明显优于MF模型。我们提供了定性的结果，解释了辅因如何改善推断因素的质量，并描述了它提供最显着改善的情况。
Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, CoFactor, which jointly decomposes the user-item interaction matrix and the item-item co-occurrence matrix with shared item latent factors. For each pair of items, the co-occurrence matrix encodes the number of users that have consumed both items. CoFactor is inspired by the recent success of word embedding models (e.g., word2vec) which can be interpreted as factorizing the word co-occurrence matrix. We show that this model significantly improves the performance over MF models on several datasets with little additional computational overhead. We provide qualitative results that explain how CoFactor improves the quality of the inferred factors and characterize the circumstances where it provides the most significant improvements.