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Movie Recommendation System for Educational Purposes Based on Field-Aware Factorization Machine

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Abstract

With rich resources, movies have been applied as instructional media in the domain of education, such as fields of Second/Foreign Language Leaning, Communication, and Media Art. Factorization machine (FM) can effectively simulate common matrix factorization models by changing the form of real-value vector, which can be utilized in movies recommendation under the context of education. However, it is usually used to solve classification tasks. This paper applies the field-aware factorization machine (FFM) to solve movie rating prediction and help users select appropriate movies for learning purposes. In order to further enhance the availability of the model, clustering algorithm is also integrated in FFM for adding new fields. The experimental results demonstrate the effectiveness of the proposed methods in reducing the RMSE.

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Funding

This research is partly supported by The Heilongjiang Higher Education Teaching Reform Project (SJGY20200320), The National Natural Science Foundation of China (No.60903083), The Scientific Research Foundation for The Overseas Returning Person of Heilongjiang Province of China (LC2018030), The Fundamental Research Foundation for Universities of Heilongjiang Province (JMRH2018XM04).

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Authors and Affiliations

  1. School of Foreign Languages, Harbin University of Science and Technology, Harbin, Heilongjiang, China

    Fei Lang

  2. Research Center of Information Security and Intelligent Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China

    Fei Lang, Lili Liang, Kai Huang, Teng Chen & Suxia Zhu

  3. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China

    Lili Liang, Kai Huang, Teng Chen & Suxia Zhu

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  1. Fei Lang
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  2. Lili Liang
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  3. Kai Huang
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  5. Suxia Zhu
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Correspondence to Fei Lang.

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Lang, F., Liang, L., Huang, K. et al. Movie Recommendation System for Educational Purposes Based on Field-Aware Factorization Machine. Mobile Netw Appl 26, 2199–2205 (2021). https://doi.org/10.1007/s11036-021-01775-9

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