关键词: word2vec, 推荐系统, 非文本化序列, 用户行为, TextRank

Abstract: Using user behavior data, adopting effective recommendation methods, and offering individualized recommendation methods are the strategy adopted generally by social network platforms, while the effectiveness of recommendation methods is the key that decides the quality of recommendation services. Methods based on matrix decomposition and methods based on collaborating filter are difficult to be promoted and applied on a large scale due to such bottlenecks as difficulty in sparsity and over-fitting. Based on the research on similarity and association between neighboring behaviors in the user behavior sequence, this paper digs the TextRank of internal structural relationship among words, and puts forward a new user behavior recommendation method by incorporating word2vec. Analysis and experiment results show that the new recommendation method is better than the traditional recommendation methods and is improved in each index, which verifies the validity and accuracy of the new method.

Key words: word2vec, recommendation system, non-textual sequences, user behavior, TextRank