A Serendipity-Oriented Greedy Algorithm for Recommendations

Denis Kotkov, Jari Veijalainen, Shuaiqiang Wang

Abstract

Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most common metric.

References

  1. Adamopoulos, P. and Tuzhilin, A. (2014). On unexpectedness in recommender systems: Or how to better expect the unexpected. ACM Transactions on Intelligent Systems and Technology, 5(4):1-32.
  2. Amatriain, X. and Basilico, J. (2015). Recommender systems in industry: A netflix case study. In Recommender Systems Handbook, pages 385-419. Springer.
  3. Castells, P., Hurley, N. J., and Vargas, S. (2015). Novelty and diversity in recommender systems. In Recommender Systems Handbook, pages 881-918. Springer.
  4. Celma Herrada, O. (2009). Music recommendation and discovery in the long tail. PhD thesis, Universitat Pompeu Fabra.
  5. Ekstrand, M. D., Ludwig, M., Konstan, J. A., and Riedl, J. T. (2011). Rethinking the recommender research ecosystem: Reproducibility, openness, and lenskit. In Proceedings of the 5th ACM Conference on Recommender Systems, pages 133-140, New York, NY, USA. ACM.
  6. Harper, F. M. and Konstan, J. A. (2015). The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems, 5(4):19:1-19:19.
  7. Iaquinta, L., Semeraro, G., de Gemmis, M., Lops, P., and Molino, P. (2010). Can a recommender system induce serendipitous encounters? InTech.
  8. Järvelin, K. and Kekäläinen, J. (2000). Ir evaluation methods for retrieving highly relevant documents. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 41-48, New York, NY, USA.
  9. Kaminskas, M. and Bridge, D. (2014). Measuring surprise in recommender systems. In Proceedings of the Workshop on Recommender Systems Evaluation: Dimensions and Design (Workshop Programme of the 8th ACM Conference on Recommender Systems).
  10. Kotkov, D., Veijalainen, J., and Wang, S. (2016a). Challenges of serendipity in recommender systems. In Proceedings of the 12th International conference on web information systems and technologies., volume 2, pages 251-256. SCITEPRESS.
  11. Kotkov, D., Wang, S., and Veijalainen, J. (2016b). A survey of serendipity in recommender systems. KnowledgeBased Systems, 111:180-192.
  12. Lu, Q., Chen, T., Zhang, W., Yang, D., and Yu, Y. Serendipitous personalized ranking for top-n recommendation. In Proceedings of the The IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, volume 1.
  13. Maksai, A., Garcin, F., and Faltings, B. (2015). Predicting online performance of news recommender systems through richer evaluation metrics. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 179-186, New York, NY, USA. ACM.
  14. Ricci, F., Rokach, L., and Shapira, B. (2011). Recommender Systems Handbook, chapter Introduction to Recommender Systems Handbook, pages 1-35. Springer US.
  15. Tacchini, E. (2012). Serendipitous mentorship in music recommender systems. PhD thesis.
  16. Zhang, Y. C., Séaghdha, D. O., Quercia, D., and Jambor, T. (2012). Auralist: Introducing serendipity into music recommendation. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining, pages 13-22, New York, NY, USA. ACM.
  17. Zheng, Q., Chan, C.-K., and Ip, H. H. (2015). An unexpectedness-augmented utility model for making serendipitous recommendation. In Advances in Data Mining: Applications and Theoretical Aspects, volume 9165, pages 216-230. Springer International Publishing.
  18. Ziegler, C.-N., McNee, S. M., Konstan, J. A., and Lausen, G. (2005). Improving recommendation lists through topic diversification. InProceedings of the 14th International Conference on World Wide Web, pages 22- 32, New York, NY, USA. ACM.
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Paper Citation


in Harvard Style

Kotkov D., Veijalainen J. and Wang S. (2017). A Serendipity-Oriented Greedy Algorithm for Recommendations . In Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-246-2, pages 32-40. DOI: 10.5220/0006232800320040


in Bibtex Style

@conference{webist17,
author={Denis Kotkov and Jari Veijalainen and Shuaiqiang Wang},
title={A Serendipity-Oriented Greedy Algorithm for Recommendations},
booktitle={Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2017},
pages={32-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006232800320040},
isbn={978-989-758-246-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - A Serendipity-Oriented Greedy Algorithm for Recommendations
SN - 978-989-758-246-2
AU - Kotkov D.
AU - Veijalainen J.
AU - Wang S.
PY - 2017
SP - 32
EP - 40
DO - 10.5220/0006232800320040