Affiliations: [a] Faculty of Big Data and Artificial Intelligence, An Hui Xin Hua University, Hefei, Anhui Province, China
| [b] Anhui Jianzhu University, Hefei, Anhui Province, China
| [c] Hu Nan Zhong Yi Yao University, Changsha, Hunan Province, China
| [d] Faculty of Data Science, City University of Macau, Macau, China
Correspondence:
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Corresponding author. Zuobin Ying, Faculty of Data Science, City University of Macau, Macau, China. E-mail: zbying@cityu.mo.
Abstract: Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model.
