摘要:根据用户的历史评分数据为用户提供推荐的商品列表，是目前推荐系统研究的主流.研究者发现，随着用户参与度的不断提高，将反映用户偏好的评论文本与评分数据结合，可以进一步提高推荐的质量.提出了基于潜在特征同步学习和偏好引导的商品推荐方法，将评论文本的主题与用户的"打分偏好"进行关联，同步学习用户评论文本的潜在主题、评分矩阵的用户潜在因子和商品潜在因子，并将潜在主题作为用户个人偏好引导来约束推荐方法对商品的预测打分.该方法对推荐质量的优化主要体现在两个方面：一是在评论文本的潜在主题和评分数据的两种潜在因子之间建立映射关系，同步求解主题模型和矩阵分解模型；二是将从评论文本中学习得到的潜在主题作为用户对商品的个性偏好引入到矩阵分解中，进一步优化推荐方法.在来自Amazon网站的28组真实数据集上进行实验，以均方误差为评价指标，与已有的模型进行了对比分析.实验结果表明，该方法有效减少了推荐误差，与已有的TopicMF方法相比，均方误差在数据子集上最大减少了3.32%，平均减少了0.92%.



Abstract:It is a popular way that makes use of users' rating data to recommend products or items to users. Currently, more and more users have contributed their reviews to recommender system for better online shopping experiences. Researchers have become interested in using review texts as extra information to improve recommendation quality. It is argued that reviews written by a user implicitly represent his/her preferences. In this study, a preference guidance recommendation approach is proposed that simultaneously learns latent factors from rating data and latent topics from review texts. More specifically, the learned latent topics are assumed to be positively correlated with both of the corresponding user factors and item factors, which can further improve the accuracy of recommendation prediction. The proposed approach has two advantages. One is that in order to capture such a dependent correlation, a transformation function is used for simultaneously learning latent features, i.e., latent factors and latent topics. The other is that the predicted ratings of items are influenced by the implicit tastes of users, i.e., the latent topics from review texts. Experiments are conducted on the data from Amazon consisting of 28 categories. Experimental results show that the proposed approach obtains 3.32% improvement than the recent TopicMF approach in some category dataset and the average improvement is 0.92% in terms of mean square error.



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