Recommendation Algorithm for Post-Context Filtering Based on TF-IDF: Case Study of Catering O2O
Yin Cong1, Zhang Liyi2()
1Intellectual Property School, Chongqing University of Technology, Chongqing 400054, China 2School of Information Management, Wuhan University, Wuhan 430072, China
[Objective] This paper carries out an in-depth study on context-integrated and personalized recommendation, aiming to address the issue of information overload. [Methods] We proposed a new contextual preference prediction model based on TF-IDF algorithm for post-context filtering, as well as the contextual association probability and universal importance. Then, we adjusted the initial scores of traditional recommendation with the help of item category preferences to generate the final list. [Results] We conducted an empirical study on catering industry and found that the proposed algorithm yielded better results. [Limitations] The accuracy of the context association needs to be improved. [Conclusions] Context information plays an important role in user behavior and decision making. More research is needed to improve the personalized recommendation based on context modeling.
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Yin Cong,Zhang Liyi. Recommendation Algorithm for Post-Context Filtering Based on TF-IDF: Case Study of Catering O2O. Data Analysis and Knowledge Discovery, 2018, 2(11): 28-36.
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