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An interpretable neural network TV program recommendation based on SHAP

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Abstract

With the development of artificial intelligence, many fields are trying to solve problems with the powerful representation ability of neural networks. Recently, recommendation systems based on neural networks have become increasingly popular and the applications are expanding, especially in TV program recommendations. However, the opacity of the neural networks has resulted in users being unable to fully trust the predicted recommendations, which increases the need for interpretable recommendation systems. This paper analyzes the interpretability of a recommendation model based on neural networks. We propose a convolutional neural TV program recommendation based on auxiliary information (CNPR-AI) to learn the program features effectively. First, we construct program dictionaries and leverage word embeddings to learn textual auxiliary information to generate program representations. We further learn program representations to generate user representations with convolutional neural networks. Then we input the program representation and user representation into the prediction module to obtain the recommendation results. As SHapley Additive exPlanations (SHAP) can provide interpretation solutions for deep learning, we utilize it to generate visual interpretations for our model to show the role played by each TV program feature in predicting user interests. We believe that the interpretations developed can help users better understand the learning mechanisms of the neural network and reflect different users’ preferences.

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Data availability

The data that support the findings of this study are available from Beijing Gehua CATV Network Co., Ltd. but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Beijing Gehua CATV Network Co., Ltd.

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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, 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, Ruiling Fu, Xiaoli Feng, Tongtong Xing & Meiqi Ji

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  1. Fulian Yin
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Correspondence to Meiqi Ji.

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Yin, F., Fu, R., Feng, X. et al. An interpretable neural network TV program recommendation based on SHAP. Int. J. Mach. Learn. & Cyber. 14, 3561–3574 (2023). https://doi.org/10.1007/s13042-023-01850-5

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