计算机工程与应用2019，Vol.55Issue(4)：142-147,158,7.DOI:10.3778/j.issn.1002-8331.1711-0144
基于深度学习的用户行为推荐方法研究
Research on User Behavior Recommendation Method Based on Deep Learning
摘要
Abstract
Using user behavior data, adopting effective recommendation methods, and offering individualized recommen-dation 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 for-ward 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.关键词
word2vec/推荐系统/非文本化序列/用户行为/TextRankKey words
word2vec/ recommendation system/ non-textual sequences/ user behavior/ TextRank分类
信息技术与安全科学引用本文复制引用
张祖平,沈晓阳..基于深度学习的用户行为推荐方法研究[J].计算机工程与应用,2019,55(4):142-147,158,7.基金项目
国家自然科学基金(No.61379109). （No.61379109）
