【摘要】 推荐系统是解决互联网信息过载问题的有效途径之一,其中具有代表性的是协同过滤推荐。传统的协同过滤推荐方法只考虑评分信息,而评论信息则包含了用户和物品更具体的特征信息。使用主题模型LDA并结合评分信息和评论信息,提出了一种基于用户改进的LDA算法。假设每个用户下隐含着主题分布,主题下隐含着物品分布,同时词语的分布由主题和物品共同决定,该算法根据潜在主题分布挖掘用户兴趣进而完成推荐。实验结果表明,改进的算法有效提升了推荐质量。更多还原

【Abstract】 Recommender system is one of the effective ways to solve the problem of information overload.The collaborative filtering method is a typical method of recommender systems.The traditional collaborative filtering algorithm only takes rating information into account,while reviews contain more specific characteristic information about users and items.In this paper,we proposed an improved LDA algorithm which can combine ratings with review opinions of users.We assumed that each user has an implicit topic distribution,each topic has an implicit item distribution,and the distribution of words is determined by the topic and the item,then we used the potential topic distribution to mine user’s interests and make recommendations.The experiment shows that our algorithm can effectively improve the recommendation quality.更多还原