Abstract
Current movie recommender system is hard to capture user’s preference due to the multidimensional and dynamic characteristics. Aiming at this problem, in this paper, we propose a dynamic hybrid movie recommender framework which models user’s preference from four different aspects. The framework is organized according to the classic two-stage information retrieval dichotomy: first, we adopt a suitable recommender algorithm for each aspect respectively for candidate generation, and then a linear combination model is designed to produce the final recommendation list. In order to capture the dynamics of user’s preference, We also constructe a feedback learning mechanism which utilize the utility function to compute the best weight vector for each recommender algorithm. Case study on our framework shows that our model can accurately capture user’s current interest with acceptable cost.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China under Grant Numbers 61632009, 61472451 and 61272151, and the High Level Talents Program of Higher Education in Guangdong Province under Funding Support Number 2016ZJ01.
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Liu, X., Wang, G., Jiang, W., Long, Y. (2016). DHMRF: A Dynamic Hybrid Movie Recommender Framework. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_37
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DOI: https://doi.org/10.1007/978-3-319-49178-3_37
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