A TF-IDF Based Contextual Post-Filtering Recommendation Algorithm in O2O Environment: Take the Catering Industry as an Example

摘要：

　　O2O accelerates the integration of online and offline,promotes the upgrading of industrial structure and consumption pattern, meanwhile brings the information overload problem.This paper develops a post-context filtering recommendation algorithm based on TF-IDF, which improves the existing algorithms.Combined with contextual association probability and contextual universal importance, a contextual preference prediction model was constructed to adjust the initial score of the traditional recommendation combined with item category preference to generate the final result.The example of the catering industry shows that the proposed algorithm is more effective than the improved algorithm.

关键词： Context Information Contextual Post-Filtering Recommendation TF-IDF Contextual Preference Item Category Preference

作者: Yin Cong Tu Meng Zhang Liyi

作者单位: Chongqing Intellectual Property School,Chongqing University of Technology, Chongqing, China School of Information Management,Wuhan University,Wuhan, China

会议类型: 国际会议

会议名称: the 12th International Conference on Management of e-Commerce and e-Government( ICMeCG 2018) (第十二届电子商务与电子政务管理国际会议)

会议地点: 郑州

会议语种:英文

页码: 321-326

在线出版日期: 2018-09-21（万方平台首次上网日期，不代表论文的发表时间）