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Neural TV program recommendation with heterogeneous attention

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Abstract

TV program recommendation is very important to avoid confusing users with large amounts of information. The existing methods are mainly based on collaborative filtering to utilize the interaction between users and items. However, they ignore auxiliary information that contains rich semantic information. In this paper, we propose a neural TV program recommendation with heterogeneous attention, which incorporates the multi-level features of auxiliary information and neural networks based on attention mechanism to obtain accurate program and user representations. In the program encoder module, we learn the different semantic information of labels and titles contained in each program through a neural network with heterogeneous attention to identify multi-hierarchical program information. In the user encoder module, we incorporate auxiliary information and interactions between users and programs. In addition, we utilize a personalized attention mechanism to learn the importance of different programs for each user to reveal user preferences. Specifically, we collect and process user viewing data in the capital of China to provide a real scenario for personalized recommendation. Experiments on real dataset show that our method can effectively improve the effectiveness of TV program recommendations than other existing models.

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Notes

  1. We omit the dimensions of hidden layers because they are usually close to the word embedding dimension D.

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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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Authors and Affiliations

  1. State Key Laboratory of Media Convergence and Communication; School of Information and Communication Engineering, Communication University of China, Beijing, 100024, China

    Fulian Yin

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

    Meiqi Ji, Sitong Li & Yanyan Wang

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  1. Fulian Yin
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  2. Meiqi Ji
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  3. Sitong Li
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  4. Yanyan Wang
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Contributions

FLY contributed conceptualization, methodology and supervision; MQJ contributed formal analysis, methodology, software and writing; STL contributed investigation, validation and writing; YYW contributed validation, visualization, supervision and writing.

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Correspondence to Yanyan Wang.

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Yin, F., Ji, M., Li, S. et al. Neural TV program recommendation with heterogeneous attention. Knowl Inf Syst 64, 1759–1779 (2022). https://doi.org/10.1007/s10115-022-01695-4

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