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Link to original content: https://unpaywall.org/10.1007/978-3-031-53503-1_17
Exploring the Power of Weak Ties on Serendipity in Recommender Systems | SpringerLink
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Exploring the Power of Weak Ties on Serendipity in Recommender Systems

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Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1144))

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Abstract

With our increasingly refined online browsing habits, the demand for high-grade recommendation systems has never been greater. Improvements constantly target general performance, evaluation, security, and explainability, but optimizing for serendipitous experiences is imperative since a serendipity-optimized recommender helps users discover unforeseen relevant content. Given that serendipity is a form of genuine unexpected experiences and recommenders are facilitators of user experiences, we aim at leveraging weak ties to explore their impact on serendipity. Weak links refer to social connections between individuals or groups that are not closely related or connected but can still provide valuable information and opportunities. On the other hand, the underlying social structure of recommender datasets can be misleading, rendering traditional network-based approaches ineffective. For that, we developed a network-inspired clustering mechanism to overcome this obstacle. This method elevates the system’s performance by optimizing models for unexpected content. By leveraging group weak ties, we aim to provide a novel perspective on the subject and suggest avenues for future research. Our study can also have practical implications for designing online platforms that enhance user experience by promoting unexpected discoveries.

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References

  1. Jannach, D., Jugovac, M.: Measuring the business value of recommender systems. ACM Trans. Manage. Inf. Syst. (TMIS) 10(4), 1–23 (2019)

    Article  Google Scholar 

  2. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput, Surv. (CSUR) 52(1), 1–38 (2019)

    Article  Google Scholar 

  3. Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J.R., Zhang, Y.-C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. 107(10), 4511–4515 (2010)

    Article  Google Scholar 

  4. Argyriou, A., González-Fierro, M., Zhang, L.: Microsoft recommenders: best practices for production-ready recommendation systems. In: 2020 Companion Proceedings of the Web Conference, pp. 50–51 (2020)

    Google Scholar 

  5. Ricci, F., Rokach, L., Shapira, B.: Recommender systems: techniques, applications, and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook. Springer, New York (2022). https://doi.org/10.1007/978-1-0716-2197-4_1

  6. Nabizadeh, A., Jorge, A., Leal, J.P.: Long term goal oriented recommender systems. In: 11th International Conference on Web Information Systems and Technologies (2015)

    Google Scholar 

  7. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)

    Article  Google Scholar 

  8. McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI’06 Extended Abstracts on Human Factors in Computing Systems, pp. 1097–1101 (2006)

    Google Scholar 

  9. Ma, T., Wang, X., Zhou, F., Wang, S.: Research on diversity and accuracy of the recommendation system based on multi-objective optimization. Neural Comput. Appl. 35(7), 5155–5163 (2023)

    Article  Google Scholar 

  10. Yannam, V.R., Kumar, J., Babu, K.S., Patra, B.K.: Enhancing the accuracy of group recommendation using slope one. J. Supercomput. 79(1), 499–540 (2023)

    Article  Google Scholar 

  11. Al Jurdi, W., Abdo, J.B., Demerjian, J., Makhoul, A.: Critique on natural noise in recommender systems. ACM Trans. Knowl. Disc. Data (TKDD) 15(5), 1–30 (2021)

    Article  Google Scholar 

  12. Al Jurdi, W., Abdo, J.B., Demerjian, J., Makhoul, A.: Strategic attacks on recommender systems: an obfuscation scenario. In: 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), pp. 1–8. IEEE (2022)

    Google Scholar 

  13. Al Jurdi, W., Abdo, J.B., Demerjian, J., Makhoul, A.: Group validation in recommender systems: framework for multi-layer performance evaluation. arXiv preprint arXiv:2207.09320 (2022)

  14. Pramod, D.: Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda. Data Technol. Appl. 57(1), 32–55 (2023)

    Google Scholar 

  15. Chatti, M.A., Guesmi, M., Muslim, A.: Visualization for recommendation explainability: a survey and new perspectives. arXiv preprint arXiv:2305.11755 (2023)

  16. Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)

    Article  Google Scholar 

  17. Duricic, T., Lacic, E., Kowald, D., Lex, E.: Exploiting weak ties in trust-based recommender systems using regular equivalence. arXiv preprint arXiv:1907.11620 (2019)

  18. Ramakrishnan, N., Keller, B.J., Mirza, B.J., Grama, A.Y., Karypis, G.: When being weak is brave: privacy in recommender systems. arXiv preprint cs/0105028 (2001)

    Google Scholar 

  19. Shokeen, J., Rana, C.: Social recommender systems: techniques, domains, metrics, datasets and future scope. J. Intell. Inf. Syst. 54(3), 633–667 (2020)

    Article  Google Scholar 

  20. Al Jurdi, W., El Khoury Badran, M., Jaoude, C.A., Abdo, J.B., Demerjian, J., Makhoul, A.: Serendipity-aware noise detection system for recommender systems. In: Proceedings of the Information and Knowledge Engineering (2018)

    Google Scholar 

  21. Kotkov, D., Medlar, A., Glowacka, D.: Rethinking serendipity in recommender systems. In: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, pp. 383–387 (2023)

    Google Scholar 

  22. Wang, Y., Min, Y., Chen, X., Ji, W.: Multi-view graph contrastive representation learning for drug-drug interaction prediction. In: Proceedings of the Web Conference 2021, pp. 2921–2933 (2021)

    Google Scholar 

  23. Ziarani, R.J., Ravanmehr, R.: Serendipity in recommender systems: a systematic literature review. J. Comput. Sci. Technol. 36, 375–396 (2021)

    Article  Google Scholar 

  24. Yan, E.: Serendipity: accuracy’s unpopular best friend in recommender systems. Towards Data Science, April 2020

    Google Scholar 

  25. Bhandari, U., Sugiyama, K., Datta, A., Jindal, R.: Serendipitous recommendation for mobile apps using item-item similarity graph. In: Banchs, R.E., Silvestri, F., Liu, T.-Y., Zhang, M., Gao, S., Lang, J. (eds.) AIRS 2013. LNCS, vol. 8281, pp. 440–451. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45068-6_38

    Chapter  Google Scholar 

  26. Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., Naumann, F.: A serendipity model for news recommendation. In: Hölldobler, S., Krötzsch, M., Peñaloza, R., Rudolph, S. (eds.) KI 2015. LNCS (LNAI), vol. 9324, pp. 111–123. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24489-1_9

    Chapter  Google Scholar 

  27. Gower, J.C.: A general coefficient of similarity and some of its properties. Biometrics 27, 857–871 (1971)

    Article  Google Scholar 

  28. Panagiotis, A.: On unexpectedness in recommender systems: or how to expect the unexpected. In: Proceedings of the Workshop on Novelty and Diversity in Recommender Systems (DiveRS 2011), at the 5th ACM International Conference on Recommender Systems (RecSys 2011), pp. 11–18 (2011)

    Google Scholar 

  29. Müllner, D.: Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.2378 (2011)

  30. Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1–19 (2015)

    Google Scholar 

  31. Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39718-2_23

    Chapter  Google Scholar 

  32. Wang, X., He, X., Wang, M., Feng, F., Chua, T.-S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2019)

    Google Scholar 

  33. Al Jurdi, W., Abdo, J.B.: GitHub Repository: Optimizing Recommendations: A Contemporary Networks-inspired Approach, June 2023

    Google Scholar 

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Acknowledgments

This work has been supported by the Lebanese University Research Program.

Author information

Authors and Affiliations

  1. University Bourgogne Franche-Comté, FEMTO-ST Institute, CNRS, Montbéliard, France

    Wissam Al Jurdi & Abdallah Makhoul

  2. Lebanese University, LaRRIS, Faculty of Sciences, Fanar, Lebanon

    Wissam Al Jurdi & Jacques Demerjian

  3. University of Cincinnati, Cincinnati, USA

    Jacques Bou Abdo

Authors
  1. Wissam Al Jurdi
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  2. Jacques Bou Abdo
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  3. Jacques Demerjian
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  4. Abdallah Makhoul
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Corresponding author

Correspondence to Wissam Al Jurdi .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

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Al Jurdi, W., Bou Abdo, J., Demerjian, J., Makhoul, A. (2024). Exploring the Power of Weak Ties on Serendipity in Recommender Systems. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-53503-1_17

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-53503-1

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