Frontiers of Data and Domputing ›› 2021, Vol. 3 ›› Issue (4): 54-69.doi: 10.11871/jfdc.issn.2096-742X.2021.04.005

• Special Issue: Visualization and Visual Analysis • Previous Articles     Next Articles

Interactive Movie Recommendation System Based on Local Model Fusion

WANG Youyan(),SUN Kanggao(),TANG Ying()   

  1. Department of Computer Science &Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310000, China
  • Received:2021-06-10 Online:2021-08-20 Published:2021-08-30
  • Contact: TANG Ying E-mail:ping_o96@qq.com;sunkanggao@163.com;ytang@zjut.edu.cn

Abstract:

[Objective] To improve user experience and confidence in the recommendation system, and help users understand the cause of recommendation, we design and implement an interactive visual recommendation system. [Methods] Firstly, we extract the preference features of each user from the user's history viewing tag set, and allow all users to be clustered into different subgroups based on these preference features using the LDA topic model. After that, we apply the SLIM sparse linear model to train the local recommendation model separately for each user subgroup. Finally, we use the contextual semantic information of the training process to design and implement the interactive movie recommendation system. [Results] This system, RecVis, can visualize the semantic information of recommendation and user portraits, provide recommendation explanation and interactive feedback function, and obtain the latest recommendation according to the user's interactive feedback in real-time. [Conclusions] The test on the Douban movie dataset proves the effectiveness of the system in terms of recommendation. In addition, a series of case studies verify that RecVis can help users understand the recommendation results and increase trust in the recommendation system.

Key words: model fusion, SLIM, topic model, user portrait, interactive movie recommendation