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Open Access

Personalized Real-Time Movie Recommendation System: Practical Prototype and Evaluation

Nanjing University of Posts and Telecommunications (NUPT), Nanjing 210003, China.
Networked Information Systems Laboratory, Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Tokyo 163-8001, Japan.
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With the eruption of big data, practical recommendation schemes are now very important in various fields, including e-commerce, social networks, and a number of web-based services. Nowadays, there exist many personalized movie recommendation schemes utilizing publicly available movie datasets (e.g., MovieLens and Netflix), and returning improved performance metrics (e.g., Root-Mean-Square Error (RMSE)). However, two fundamental issues faced by movie recommendation systems are still neglected: first, scalability, and second, practical usage feedback and verification based on real implementation. In particular, Collaborative Filtering (CF) is one of the major prevailing techniques for implementing recommendation systems. However, traditional CF schemes suffer from a time complexity problem, which makes them bad candidates for real-world recommendation systems. In this paper, we address these two issues. Firstly, a simple but high-efficient recommendation algorithm is proposed, which exploits users’ profile attributes to partition them into several clusters. For each cluster, a virtual opinion leader is conceived to represent the whole cluster, such that the dimension of the original user-item matrix can be significantly reduced, then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results. Compared to traditional clustering-based CF recommendation schemes, our method can significantly reduce the time complexity, while achieving comparable recommendation performance. Furthermore, we have constructed a real personalized web-based movie recommendation system, MovieWatch, opened it to the public, collected user feedback on recommendations, and evaluated the feasibility and accuracy of our system based on this real-world data.


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Tsinghua Science and Technology
Pages 180-191
Cite this article:
Zhang J, Wang Y, Yuan Z, et al. Personalized Real-Time Movie Recommendation System: Practical Prototype and Evaluation. Tsinghua Science and Technology, 2020, 25(2): 180-191.








Web of Science






Received: 03 June 2018
Revised: 23 August 2018
Accepted: 01 September 2018
Published: 02 September 2019
© The author(s) 2020

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