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HGER: a heterogeneous information-based recommendation with graph enhanced representation for TV program

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Abstract

With the development of online programming, the broadcast TV industry has experienced some negative impacts. The program recommendation has become crucial to boost the industry’s construction. While there have been many researches on program recommendation, there are still challenges to overcome such as alleviating the sparsity of user interaction data and improving the dynamics and generalizability of the recommendation model. In this paper, we propose a novel approach for TV program recommendation called heterogeneous information-based recommendation with graph enhanced representation (HGER). The HGER model mainly consists of two main modules. One is the program encoder which uses the program’s heterogeneous information and attention mechanism to extract personalized content and considers high-order neighbor program representations through a graph structure. The other is the user encoder which utilizes the user’s historical viewing behaviors and combines it with the graph structure to represent the high-order neighbor user. Thus, we implement an enhanced representation for both the program and user. Through extensive experiments on a real-world dataset, the results of AUC, NDCG and HR demonstrate that our approach can effectively enhance the dynamics and generalizability of the model for TV program recommendations.

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Data Availibility Statement

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Acknowledgements

The work was supported by the National Key Research and Development Program (No. 2021YFF0901705, 2021YFF0901700); the State Key Laboratory of Media Convergence and Communication, Communication University of China; the Fundamental Research Funds for the Central Universities; the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing (Internet Information, Communication University of China).

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  1. State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China

    Fulian Yin

  2. School of Information and Communication Engineering, Communication University of China, Beijing, 100024, China

    Fulian Yin, Tongtong Xing, Zebin Yao, Ruiling Fu & Sitong Li

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Yin, F., Xing, T., Yao, Z. et al. HGER: a heterogeneous information-based recommendation with graph enhanced representation for TV program. Multimed Tools Appl 83, 19391–19414 (2024). https://doi.org/10.1007/s11042-023-16315-8

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