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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 10/2023

07-05-2023 | Original Article

An interpretable neural network TV program recommendation based on SHAP

Authors: Fulian Yin, Ruiling Fu, Xiaoli Feng, Tongtong Xing, Meiqi Ji

Published in: International Journal of Machine Learning and Cybernetics | Issue 10/2023

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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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Metadata
Title
An interpretable neural network TV program recommendation based on SHAP
Authors
Fulian Yin
Ruiling Fu
Xiaoli Feng
Tongtong Xing
Meiqi Ji
Publication date
07-05-2023
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 10/2023
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01850-5

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