采用评论挖掘修正用户评分的改进协同过滤算法

针对当前电子商务网站用户评分过于集中而区分度不明显，以及整数评分可信度不高导致协同过滤推荐效果较差的问题，提出一种改进协同过滤算法. 利用改进的词性路径模板算法挖掘评论中包含的产品特征和情感词，分析并建立评论特征偏好向量；依据评论特征偏好向量计算评论中包含的情感态度，利用用户评论中包含的情感态度对评分进行修正，使得修正后的评分更接近于用户的真实评分意愿；利用修正后的评分计算评分相似度，与偏好相似度结合产生推荐. 实验结果表明，该算法有效地增加了评分区分度与可信度，提高了最近邻居的质量，从而提高了推荐结果的准确度.

Improved collaborative filtering algorithm to revise users' rating by review mining

An improved collaborative filtering algorithm was proposed aiming at the problems that user ratings for current e-commerce websites are too concentrated, the distinguishing degree is not obvious, and the credibility of the integer rating is not very well. Firstly, the improved part-of-speech path template algorithm was used to mine the product features and sentiment words contained in user’s reviews, and then the preference vector of the review was analyzed and established. Secondly, the emotional attitudes included in the review were calculated according to the review’s preference vector, and the emotional attitudes were used to revise the user’s rating, so that the revised rating was closer to the user’s true score willingness. Finally, the revised rating similarity and the preference similarity were combined to produce recommendations. The experimental results show that the proposed algorithm can effectively increase the classification and credibility of user’s ratings, and thus improves the quality of the nearest neighbors, as well as the accuracy of the recommended results.