推荐系统(recommender system)广泛应用于电子商务网站。目前流行的基于协同过滤的推荐算法利用用户的历史评分来预测用户对物品的喜好程度。随着互联网的发展,如今的电子商务网站越来越注重与用户的交互,于是产生了大量的用户生成内容(user generated content),如评论、地理位置、好友关系等。相对评分来说,用户对物品的评论从用户或者物品的各个角度具体表达了用户的观点。利用这些信息更有助于挖掘用户的喜好。该文提出一种基于词向量的方法挖掘用户评论信息,并结合协同过滤的方法设计新的推荐算法,来改善评分预测的效果。实验结果表明,该算法较大程度上提高了评分预测精度。
Abstract
Recommender system is widely used in e-commerce web sites. Traditional recommendation algorithms, e.g. collaborative filtering, predict the degree of user preference to an item based on user scoring history. Due to the development of the Internet, e-commerce websites pay more attention to user interactions, which leads to a great deal of user generated contents like comments, geographic locations and social relationships. Compared to the user rating, user comment demonstrates their opinions on different facets of the item. By taking full advantage of user generated contents, user preference can be further discovered. In this paper, we proposed an approach to using word-embedding to analyze review comments and design a novel system to predict the scores. Empirical experiments on a large review dataset show that the proposed approach can effectively improve the precision of the recommender system.
关键词
推荐系统 /
评分预测 /
词向量 /
用户评论
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Key words
recommender system /
rating prediction /
word embedding /
user comment
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