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Hyperbolic Personalized Tag Recommendation

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Book cover Database Systems for Advanced Applications (DASFAA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13246))

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Abstract

Personalized Tag Recommendation (PTR) aims to automatically generate a list of tags for users to annotate web resources, the so-called items, according to users’ tagging preferences. The main challenge of PTR is to learn representations of involved entities (i.e., users, items, and tags) from interaction data without loss of structural properties in original data. To this end, various PTR models have been developed to conduct representation learning by embedding historical tagging information into low-dimensional Euclidean space. Although such methods are effective to some extent, their ability to model hierarchy, which lies in the core of tagging information structures, is restricted by Euclidean space’s polynomial expansion property. Since hyperbolic space has recently shown its competitive capability to learn hierarchical data with lower distortion than Euclidean space, we propose a novel PTR model that operates on hyperbolic space, namely HPTR. HPTR learns the representations of entities by modeling their interactive relationships in hyperbolic space and utilizes hyperbolic distance to measure semantic relevance between entities. Specially, we adopt tangent space optimization to update model parameters. Extensive experiments on real-world datasets have shown the superiority of HPTR over state-of-the-art baselines.

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Notes

  1. 1.

    https://grouplens.org/datasets/hetrec-2011/.

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Acknowledgements

The authors would like to acknowledge the support for this work from the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17KJB520028), Tongda College of Nanjing University of Posts and Telecommunications (XK203XZ21001), and Future Network Scientific Research Fund Project (FNSRFP-2021-YB-54)

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Authors and Affiliations

  1. State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China

    Weibin Zhao, Lin Shang & Jiajun Chen

  2. Tongda College, Nanjing University of Posts and Telecommunications, Yangzhou, China

    Aoran Zhang & Yonghong Yu

  3. Department of Computer Science, Royal Holloway, University of London, Surrey, UK

    Li Zhang

  4. School of Information and Communication Technology, Griffith University, Brisbane, Australia

    Can Wang

  5. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Hongzhi Yin

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  1. Weibin Zhao
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  4. Yonghong Yu
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  5. Li Zhang
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  6. Can Wang
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Correspondence to Weibin Zhao .

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Editors and Affiliations

  1. Dept. of Computer Science&Engr., Indian Institutes of Technology, Kanpur, Uttar Pradesh, India

    Arnab Bhattacharya

  2. National University of Singapore, Singapore, Singapore

    Janice Lee Mong Li

  3. University of California, Santa Barbara, Santa Barbara, CA, USA

    Divyakant Agrawal

  4. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  5. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Mukesh Mohania

  6. Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

  7. Indraprastha Institute of Information Te, New Delhi, India

    Vikram Goyal

  8. University of Aizu, Aizu, Japan

    Rage Uday Kiran

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Zhao, W. et al. (2022). Hyperbolic Personalized Tag Recommendation. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_14

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  • DOI: https://doi.org/10.1007/978-3-031-00126-0_14

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