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Building user interest model for TV recommendation with label-based memory forgetting-enhancement model

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Abstract

TV recommendation can help users find interesting TV programs, improve user experience, and solve the problem of information overload. Current TV programs only recommend through the interactive data between users and programs ignoring the important value of temporal relation, having the problem such as data sparseness. To solve these problems, we propose a user interest model for TV recommendation with Label-based Memory Forgetting-Enhancement (LMFE). This model includes LMFE model and improved index-label interest model. The former combines memory forgetting and repetitive enhancement mechanisms, which predicts user behavior under the condition of multiple viewing indicators according to TV program labels. The latter considers multiple indexes and labels to model the user interest model, where enriches user interests and enhances the effectiveness of user interest tracing. The experiments verify the accuracy of the forecast data and recommendation results by the real TV user behavior data set. We evaluate the recommendation effect of the model through eight types of classic indexes and the results show that our model has a better prediction effect than traditional models.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61801440), the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing (Internet Information, Communication University of China), State Key Laboratory of Media Convergence and Communication (Communication University of China), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. College of Information and Communication Engineering, Communication University of China, Beijing, 100024, China

    Fulian Yin, Yanyan Pan, Pei Su & Yanyan Wang

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  1. Fulian Yin
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  2. Yanyan Pan
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  3. Pei Su
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Correspondence to Yanyan Wang.

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Yin, F., Pan, Y., Su, P. et al. Building user interest model for TV recommendation with label-based memory forgetting-enhancement model. Multimed Tools Appl 81, 26307–26330 (2022). https://doi.org/10.1007/s11042-022-12718-1

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