摘要:近年来，矩阵分解（MF）技术因其有效性和简便性在推荐系统中得到广泛应用.但是，数据稀疏和冷启动问题导致MF学习到的用户特征向量不能准确地代表用户的偏好以及反映用户间的相似关系，影响了模型的性能.为了解决该问题，规范化矩阵分解（RMF）技术引起了研究者的关注.挖掘用户间可靠的相似关系，是RMF需要解决的问题.此外，MF将目标用户特征向量和目标项目特征向量的内积作为目标用户对目标项目的评分，这种简单的线性关系忽略了用户对项目各个属性特征不同的关注度.如何分析用户对项目属性特征的关注度，获取用户更准确的偏好，仍然是一个挑战.针对上述问题，提出了基于注意力机制的规范化矩阵分解模型（ARMF）.具体地，为了获取用户间可靠的相似关系解决数据稀疏和冷启动问题，该模型同时依据用户信任网络和评分记录构建用户-项目异构网络，并基于该异构网络挖掘用户间的相似关系；为了进一步提升模型性能，通过在MF中引入注意力机制，分析用户对项目各个属性特征不同的关注度来获取用户更准确的偏好.最后，在两个真实数据集上对比ARMF与现有工作，实验结果证明，ARMF有更好的准确性和健壮性.



Abstract:In recent years, matrix factorization (MF) has been exploited commonly in recommender system because of its capability and simplification. However, data sparsity and cold-start problems make the latent feature of users learned by MF cannot represent the users' preferences and the similarity relation among users exactly, which limits the performance of MF. To remedy it, the regularized matrix factorization (RMF) draws researchers' attention. And the problem demanding prompt solution in RMF is capturing the reliable similarity relation among users. Besides, MF simply regards the inner product between the latent features of both target user and target item as the score that target user may rate the target item, ignoring the user's different attentions on various features of the item. How to analyze the user's attention on item's features and capture more accurate preference of the user is still a challenge. To address these issues, a model is put forward named attention-based regularized matrix factorization, abbreviated as ARMF. Specifically, to settle the problems of data sparsity and cold-start and obtain reliable similar relationships among users, the model builds a user-item heterogeneous network according to the social network and the rating record, and the similarities among users can be obtained based on it. Incorporating attention mechanism into MF allows us to analyze the attention of users on different item's features and capture moreaccurate preferences of users, which improves the precision of MF further. At last, the proposed model is compared with the state-of-the-art models on two real-world datasets and the result demonstrates the better precision and robustness of ARMF.



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