Skip to main content
SpringerLink
Log in

Curiosity Enhanced Bayesian Personalized Ranking for Recommender Systems

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1964))

Included in the following conference series:

  • 350 Accesses

Abstract

Curiosity affects the users’ selections of items, motivating them to explore the items regardless of their preferences. However, the existing social-based recommendation methods neglect the users’ curiosity in the social networks, and it may cause the accuracy decrease in the recommendation. Moreover, only focusing on simulating the users’ preferences can lead to users’ information cocoons. To tackle the problems above, we propose a Curiosity Enhanced Bayesian Personalized Ranking (CBPR) model for the recommender systems. The experimental results on two public datasets demonstrate the advantages of our CBPR model over the existing models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.librec.net/.

References

  1. Berlyne, D.E.: Conflict, arousal, and curiosity (1960)

    Google Scholar 

  2. Butts, C.T.: Social network analysis: a methodological introduction. Asian J. Soc. Psychol. 11, 13–41 (2007)

    Article  Google Scholar 

  3. Cai, Y., Leung, H., Li, Q., Tang, J., Li, J.: TyCo: towards typicality-based collaborative filtering recommendation. In: ICTAI, pp. 97–104 (2010). IEEE Computer Society (2010)

    Google Scholar 

  4. Fan, W., Ma, Y., Yin, D., Wang, J., Tang, J., Li, Q.: Deep social collaborative filtering. In: RecSys, pp. 305–313 (2019). ACM (2019)

    Google Scholar 

  5. Gray, R.M.: Entropy and information theory (1990)

    Google Scholar 

  6. Guo, G., Zhang, J., Sun, Z., Yorke-Smith, N.: LibRec: a java library for recommender systems. In: UMAP 2015, CEUR Workshop Proceedings, vol. 1388. CEUR-WS.org (2015)

    Google Scholar 

  7. Guo, G., Zhang, J., Thalmann, D., Yorke-Smith, N.: ETAF: an extended trust antecedents framework for trust prediction. In: ASONAM 2014, pp. 540–547. IEEE Computer Society (2014)

    Google Scholar 

  8. Guo, G., Zhang, J., Yorke-Smith, N.: A novel Bayesian similarity measure for recommender systems. In: IJCAI 2013, pp. 2619–2625. IJCAI/AAAI (2013)

    Google Scholar 

  9. Hartung, F.M., Renner, B.: Social curiosity and gossip: related but different drives of social functioning. PLoS One 8(7), e69996 (2013)

    Google Scholar 

  10. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22(1), 116–142 (2004)

    Article  Google Scholar 

  11. Jahrer, M., Töscher, A.: Collaborative filtering ensemble for ranking. In: KDD Cup 2011 competition, 2011. JMLR Proceedings, vol. 18, pp. 153–167. JMLR.org (2012)

    Google Scholar 

  12. Jøsang, A.: A logic for uncertain probabilities. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 9(03), 279–311 (2008)

    Google Scholar 

  13. Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)

    Article  Google Scholar 

  14. Lin, T., Gao, C., Li, Y.: Recommender systems with characterized social regularization. In: CIKM 2018, pp. 1767–1770. ACM (2018)

    Google Scholar 

  15. Lin, X., Zhang, M., Liu, Y., Ma, S.: A neural network model for social-aware recommendation. In: Sung, W.K., et al. (eds.) Information Retrieval Technology. AIRS 2017. LNCS, vol. 10648. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70145-5_10

  16. Loewenstein, G.: The psychology of curiosity: a review and reinterpretation. Psychol. Bullet. 116, 75–98 (1994)

    Google Scholar 

  17. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM 2011, pp. 287–296. ACM (2011)

    Google Scholar 

  18. Rafailidis, D., Crestani, F.: Joint collaborative ranking with social relationships in top-n recommendation. In: CIKM 2016, pp. 1393–1402. ACM (2016)

    Google Scholar 

  19. Rafailidis, D., Crestani, F.: Recommendation with social relationships via deep learning. In: ICTIR 2017, pp. 151–158. ACM (2017)

    Google Scholar 

  20. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI 2009, pp. 452–461. AUAI Press (2009)

    Google Scholar 

  21. Silvia, P.J.: Interest-the curious emotion. Curr. Dir. Psychol. Sci. 17(1), 57–60 (2008)

    Article  Google Scholar 

  22. Van, M., And, E., Schomaker, L.: Variants of the Borda count method for combining ranked classifier hypotheses. In: 7th International Workshop on Frontiers in Handwriting Recognition (2000)

    Google Scholar 

  23. Vega-Oliveros, D.A., Berton, L., Vazquez, F., Rodrigues, F.A.: The impact of social curiosity on information spreading on networks. In: ASONAM 2017, pp. 459–466 (2017)

    Google Scholar 

  24. Wang, X., Hoi, S.C.H., Liu, C., Ester, M.: Interactive social recommendation. In: CIKM 2017, pp. 357–366. ACM (2017)

    Google Scholar 

  25. Wu, L., Sun, P., Hong, R., Fu, Y., Wang, X., Wang, M.: SocialGCN: an efficient graph convolutional network based model for social recommendation. CoRR abs-1811-02815 (2018)

    Google Scholar 

  26. Wu, Q., Liu, S., Miao, C.: Modeling uncertainty driven curiosity for social recommendation. In: WI 2017, pp. 790–798. ACM (2017)

    Google Scholar 

  27. Wu, Q., Liu, S., Miao, C., Liu, Y., Leung, C.: A social curiosity inspired recommendation model to improve precision, coverage and diversity. In: WI 2016, pp. 240–247. IEEE Computer Society (2016)

    Google Scholar 

  28. Wu, Q., Miao, C.: Curiosity: from psychology to computation. ACM Comput. Surv. 46(2), 1–26 (2013)

    Google Scholar 

  29. Wu, W., Yin, B.: Personalized recommendation algorithm based on consumer psychology of local group purchase e-commerce users. J. Intell. Fuzzy Syst. 37(5), 5973–5981 (2019)

    Article  Google Scholar 

  30. Xu, K., Mo, J., Cai, Y., Min, H.: Enhancing recommender systems with a stimulus-evoked curiosity mechanism. IEEE Transactions on Knowledge and Data Engineering, p. 1 (2019)

    Google Scholar 

  31. Xu, Y., Xu, K., Cai, Y., Min, H.: Leveraging distrust relations to improve Bayesian personalized ranking. Inf. 9(8), 191 (2018)

    Google Scholar 

  32. Zhao, P., Lee, D.L.: How much novelty is relevant?: It depends on your curiosity. In: SIGIR 2016, pp. 315–324. ACM (2016)

    Google Scholar 

  33. Zhao, T., McAuley, J.J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: CIKM 2014, pp. 261–270. ACM (2014)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the National Natural Science Foundation of China (62076100), Fundamental Research Funds for the Central Universities, SCUT (x2rjD2230080), the Science and Technology Planning Project of Guangdong Province (2020B0101100002), CAAI-Huawei MindSpore Open Fund, CCF-Zhipu AI Large Model Fund.

Author information

Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, China

    Yaoming Deng, Qiqi Ding, Xin Wu & Yi Cai

  2. The Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education, Guangzhou, China

    Yaoming Deng, Qiqi Ding, Xin Wu & Yi Cai

Authors
  1. Yaoming Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiqi Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Cai .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deng, Y., Ding, Q., Wu, X., Cai, Y. (2024). Curiosity Enhanced Bayesian Personalized Ranking for Recommender Systems. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_24

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8141-0_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8140-3

  • Online ISBN: 978-981-99-8141-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation