关键词: 评论标签, 产品特征, 推荐算法

Abstract: The user interests and product features are extracted from comments in traditional recommendation algorithms. However, the expected recommendation performance is not achieved as it is difficult to obtain valid information, caused by the free-form and poor regularity of comments. In the current field of electronic commerce, the comment tag as a new way of comments has been widely used. Compared with comments, the comment tag has the advantages of strong regularity and information density. Thus this paper proposes a recommendation algorithm fusing comment tag which extracts the users’ opinions for the product features and then makes use of them to construct user interests model and product features model. Therefore, the proposed algorithm can recommend the products with well-reviews on specific features which users are interested in. Compared with traditional algorithms, the experimental results show that the proposed algorithm can effectively improve the user coverage and the recommendation accuracy.

Key words: comment tag, product feature, recommendation algorithm