Recommendation by Mining Multiple User Behaviors with Group Sparsity
Recommendation by Mining Multiple User Behaviors with Group Sparsity
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DOI：
10.1609/aaai.v28i1.8713
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Ting Yuan;Jian Cheng;Xi Sheryl Zhang;S. Qiu;Hanqing Lu
中科院分区：
文献类型：
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作者：
Ting Yuan;Jian Cheng;Xi Sheryl Zhang;S. Qiu;Hanqing Lu
近年来，一些推荐方法试图通过整合用户的多种行为信息来改善预测结果。如何对不同行为之间的依赖性和独立性进行建模是它们的关键。本文提出了一种新的推荐模型--组稀疏矩阵因子分解（Group-Sparse MatrixFactorization，GSMF），该模型通过组稀疏正则化将多个行为的评分矩阵分解到用户和项目的潜在因子空间中，它可以（1）为不同的行为选择出不同的潜在因子子集，解决了用户对不同行为的决策是由不同的因子集合决定的问题;（2）通过自动学习多个行为的共享因子和私有因子来建模行为之间的依赖性和独立性：（3）允许不同行为之间的共享因子是不同的，而不是所有行为共享同一组因子。在真实数据集上的实验表明，该模型能够更好地将用户的多种行为类型集成到推荐中，与其他现有的推荐模型相比具有更好的性能。
Recently, some recommendation methods try to improvethe prediction results by integrating informationfrom user’s multiple types of behaviors. How to modelthe dependence and independence between differentbehaviors is critical for them. In this paper, we proposea novel recommendation model, the Group-Sparse MatrixFactorization (GSMF), which factorizes the ratingmatrices for multiple behaviors into the user and itemlatent factor space with group sparsity regularization.It can (1) select out the different subsets of latent factorsfor different behaviors, addressing that users’ decisionson different behaviors are determined by differentsets of factors;(2) model the dependence and independencebetween behaviors by learning the sharedand private factors for multiple behaviors automatically; (3) allow the shared factors between different behaviorsto be different, instead of all the behaviors sharingthe same set of factors. Experiments on the real-world dataset demonstrate that our model can integrate users’multiple types of behaviors into recommendation better,compared with other state-of-the-arts.