Abstract
Visual information is one of the essential information for human to understand the real world. The abundant information of visual features absolutely give people many useful inferences. In movie recommendation, additional information extracted is undoubtedly beneficial for alleviating the drawback that sparseness of rating data leads to. Obviously, the visual information is such supplementary information in movie recommendation in particular. Existing context-aware movie recommendation methods normally focused on the complementary information to address the problem caused by sparseness, such as social relationship between users, reviews, attributes of movie itself. Nevertheless, there are merely a small part of researches concentrating on visual features compared to the information above. The reasons may come from two aspects: (i) the difficulties of getting useful information from movies; (ii) the difficulties of finding a proper dataset. Nowadays, the outstanding development of deep learning in computer vision fortunately help us out in the first problem. As for the second difficulty, based on the mature dataset MovieLens, we rebuild the dataset by adding movie trailers crawling from YouTube. In this paper, we propose a novel Probabilistic Matrix Factorization (PMF) model incorporating the visual information of trailers, Visual information based PMF (VPMF). Based on classic recommendation model PMF, the VPMF extracts visual features from trailers to enrich the core information furthermore ensure the accuracy. At last, a VPMF recommender system architecture is given to show how the system works.
Keywords
- Movie Recommendation
- Probabilistic Matrix Factorization (PMF)
- Recommender Systems
- Visual Feature Extraction
- Context-aware Recommendation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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This paper is supported by National Natural Science Foundation of China (Project 61372113).
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Fan, Y., Wang, Y., Yu, H., Liu, B. (2018). Movie Recommendation Based on Visual Features of Trailers. In: Barolli, L., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2017. Advances in Intelligent Systems and Computing, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-61542-4_23
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DOI: https://doi.org/10.1007/978-3-319-61542-4_23
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