【摘要】 传统协同过滤算法中,随着用户和商品数量的日益增多,用户-项目评分矩阵越来越稀疏。针对这一问题,提出了一种基于用户兴趣变化和评论的协同过滤算法。该算法将用户评论和遗忘曲线引入传统协同过滤算法中,将评论文本作为商品特征描述文本,使用主题模型计算商品主题特征,引入艾宾浩斯遗忘曲线来协同计算用户的评论分布及评论相似度。将用户评论相似度和用户评分相似度相结合,以得到最终的用户相似度,进而对商品评分进行预测。对网络爬取的真实数据进行验证,结果显示该算法能够在稀疏数据集上获得较好的推荐结果。更多还原

【Abstract】 The user-item rating matrix is becoming more and more sparse with the increasing number of users and commodities in the traditional collaborative filtering algorithm.To solve this problem,a collaborative filtering algorithm based on user interest change and comment was proposed.The algorithm introduces user comment and forgetting curve into the traditional collaborative filtering algorithm.The comment text is used as the text of commodity feature description,the topic model is used to calculate the commodity topic features,and Ebbinghaus’ s forgetting curve is also introduced for the cooperative computing of user comment distribution and comment similarity.The similarity of user comment and the similarity of user rating are combined to get the final similarity,and then the rating of commodity is predicted.The algorithm was validated by real data crawled over the network.The experimental results show that the proposed algorithm can get better recommendation results in sparse data sets than the traditional collaborative filtering algorithm.更多还原