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Regularized Framework on Heterogeneous Hypergraph Model for Personal Recommendation

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Theoretical Computer Science (NCTCS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1693))

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Abstract

Filtering-based recommendations are one of the most widely used recommendation algorithms in recent years. Most of which are based on simple graph to construct network model, and the nodes connected by edges are all pairwise relationships. In practice, some relationships are more complex than pairwise relationships. Moreover, collaborative filtering only focused on the relationships between users and users, items and items, users and items, without considering the relations between items and tags. To address these problems and improve the accuracy of the recommendation algorithms, we propose a regularized framework based on heterogeneous hypergraph, which integrates tag information into the recommendation system. Firstly, the hyperedges are built for each user and all items those are rated by the user, and then the similarity index between items in the hyperedge is calculated. Secondly, the relational graph between items and tags is constructed. Thirdly, we establish a regularization framework, and minimize the cost function for scoring prediction and recommendation. Finally, we verify the effectiveness of our proposed algorithm on Movielens-100k, Restaurant & consumer and Filmtrust datasets, and the diverse simulation results show that our proposed algorithm gains better recommendation performance.

Supported by National Natural Science Foundation of China (No. 62006149, 62003203, 62102239); Natural Science Foundation of Shaanxi Province (No. 2021JM-206, 2021JQ-314); Fundamental Research Funds For the Central Universities (No. 2021CSLY023, 2021TS035, GK202205038); Center for Applied Mathematics of Inner Mongolian (ZZYJZD2022003); the Shaanxi Key Science and Technology Innovation Team Project (No. 2022TD-26).

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Notes

  1. 1.

    https://grouplens.org/datasets/movielens/100k/.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets/Restaurant.

  3. 3.

    http://www.public.asu.edu/jtang20/datasetcode/truststudy.htm.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, Xian, China

    Tingting Zhu, Jianrui Chen, Zhihui Wang & Di Wu

  2. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Tingting Zhu, Jianrui Chen, Zhihui Wang & Di Wu

Authors
  1. Tingting Zhu
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  2. Jianrui Chen
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  3. Zhihui Wang
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  4. Di Wu
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Corresponding author

Correspondence to Jianrui Chen .

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Editors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Zhiping Cai

  2. Shanghai Jiao Tong University, Shanghai, China

    Yijia Chen

  3. Chinese Academy of Sciences, Beijing, China

    Jialin Zhang

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Zhu, T., Chen, J., Wang, Z., Wu, D. (2022). Regularized Framework on Heterogeneous Hypergraph Model for Personal Recommendation. In: Cai, Z., Chen, Y., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2022. Communications in Computer and Information Science, vol 1693. Springer, Singapore. https://doi.org/10.1007/978-981-19-8152-4_11

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  • DOI: https://doi.org/10.1007/978-981-19-8152-4_11

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  • Online ISBN: 978-981-19-8152-4

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