AntRS: Recommending Lists Through a Multi-objective Ant Colony System

  • Pierre-Edouard OscheEmail author
  • Sylvain Castagnos
  • Anne Boyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)


When people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discover from an adaptive mobile museum guide. To make these lists coherent from the users’ perspective, recommendations must find the best compromise between multiple objectives (best possible precision, need for diversity and novelty). We propose to achieve that goal through a multi-agent recommender system, called AntRS. We evaluated our approach with a music dataset with about 500 users and more than 13,000 sessions. The experiments show that we obtain good results as regards to precision, novelty and coverage in comparison with typical state-of-the-art single and multi-objective algorithms.


Recommender systems Multi-agent systems Multi-agent reinforcement learning 


  1. 1.
    Ariyasingha, I., Fernando, T.: Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem. Swarm Evol. Comput. 23, 11–26 (2015)CrossRefGoogle Scholar
  2. 2.
    Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern Information Retrieval, vol. 463. ACM Press, New York (1999)Google Scholar
  3. 3.
    Barán, B., Schaerer, M.: A multiobjective ant colony system for vehicle routing problem with time windows. In: Applied Informatics, pp. 97–102 (2003)Google Scholar
  4. 4.
    Bonnin, G., Jannach, D.: Automated generation of music playlists: survey and experiments. ACM Comput. Surv. 47(2), 26:1–26:35 (2014)CrossRefGoogle Scholar
  5. 5.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  6. 6.
    Castagnos, S., Boyer, A.: A client/server user-based collaborative filtering algorithm: model and implementation. In: 17th European Conference on Artificial Intelligence (ECAI 2006), pp. 617–621 (2006)Google Scholar
  7. 7.
    Dorigo, M., Birattari, M.: Ant colony optimization. In: Encyclopedia of Machine Learning, pp. 36–39. Springer (2011)Google Scholar
  8. 8.
    Fortes, R.S., Lacerda, A., Freitas, A., Bruckner, C., Coelho, D., Gonçalves, M.: User-oriented objective prioritization for meta-featured multi-objective recommender systems. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 311–316. ACM (2018)Google Scholar
  9. 9.
    Geng, B., Li, L., Jiao, L., Gong, M., Cai, Q., Wu, Y.: NNIA-RS: a multi-objective optimization based recommender system. Physica A 424, 383–397 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Guimarães, A., Costa, T.F., Lacerda, A., Pappa, G.L., Ziviani, N.: GUARD: a genetic unified approach for recommendation. J. Inf. Data Manage. 4(3), 295 (2013)Google Scholar
  11. 11.
    Guo, G., Zhang, J., Sun, Z., Yorke-Smith, N.: LibRec: a Java library for recommender systems. In: UMAP Workshops (2015)Google Scholar
  12. 12.
    Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latenttopic sequential patterns. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 131–138 (2012)Google Scholar
  13. 13.
    Jannach, D., Lerche, L., Kamehkhosh, I.: Beyond hitting the hits: generating coherent music playlist continuations with the right tracks. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 187–194. ACM (2015)Google Scholar
  14. 14.
    Jones, N.: User perceived qualities and acceptance of recommender systems: the role of diversity. Ph.D. thesis, EPFL (2010)Google Scholar
  15. 15.
    Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1), 2:1–2:42 (2016)CrossRefGoogle Scholar
  16. 16.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)Google Scholar
  17. 17.
    L’Huillier, A., Castagnos, S., Boyer, A.: Understanding usages by modeling diversity over time. In: 22nd Conference on User Modeling, Adaptation, and Personalization, vol. 1181 (2014)Google Scholar
  18. 18.
    Maillet, F., Eck, D., Desjardins, G., Lamere, P.: Steerable playlist generation by learning song similarity from radio station playlists. In: In Proceedings of the 10th International Conference on Music Information Retrieval (2009)Google Scholar
  19. 19.
    McGinty, L., Smyth, B.: On the role of diversity in conversational recommender systems. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 276–290. Springer, Heidelberg (2003). Scholar
  20. 20.
    Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. CoRR abs/1802.08452 (2018)CrossRefGoogle Scholar
  21. 21.
    Ribeiro, M.T., Lacerda, A., Veloso, A., Ziviani, N.: Pareto-efficient hybridization for multi-objective recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 19–26 (2012)Google Scholar
  22. 22.
    Ribeiro, M.T., Ziviani, N., Moura, E.S.D., Hata, I., Lacerda, A., Veloso, A.: Multiobjective pareto-efficient approaches for recommender systems. ACM Trans. Intell. Syst. Technol. 5(4), 53:1–53:20 (2014)CrossRefGoogle Scholar
  23. 23.
    Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook. Springer, Boston (2015). Scholar
  24. 24.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)Google Scholar
  25. 25.
    Tintarev, N., Lofi, C., Liem, C.C.: Sequences of diverse song recommendations: an exploratory study in a commercial system. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP 2017, pp. 391–392 (2017)Google Scholar
  26. 26.
    Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 109–116. ACM (2011)Google Scholar
  27. 27.
    Wang, S., Gong, M., Ma, L., Cai, Q., Jiao, L.: Decomposition based multiobjective evolutionary algorithm for collaborative filtering recommender systems. In: IEEE Congress on Evolutionary Computation (CEC), pp. 672–679 (2014)Google Scholar
  28. 28.
    Wang, S., Gong, M., Li, H., Yang, J.: Multi-objective optimization for long tail recommendation. Knowl. Based Syst. 104, 145–155 (2016)CrossRefGoogle Scholar
  29. 29.
    Xia, X., Wang, X., Li, J., Zhou, X.: Multi-objective mobile app recommendation: a system-level collaboration approach. Comput. Electr. Eng. 40(1), 203–215 (2014)CrossRefGoogle Scholar
  30. 30.
    Yang, B., Lei, Y., Liu, J., Li, W.: Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1633–1647 (2017)CrossRefGoogle Scholar
  31. 31.
    Zhang, L.: The definition of novelty in recommendation system. Int. J. Eng. Sci. Technol. Rev. 6(3), 141–145 (2013)CrossRefGoogle Scholar
  32. 32.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32. ACM (2005)Google Scholar
  33. 33.
    Zuo, Y., Gong, M., Zeng, J., Ma, L., Jiao, L.: Personalized recommendation based on evolutionary multi-objective optimization [research frontier]. IEEE Comput. Intell. Mag. 10(1), 52–62 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pierre-Edouard Osche
    • 1
    Email author
  • Sylvain Castagnos
    • 1
  • Anne Boyer
    • 1
  1. 1.Univ. of Lorraine - CNRS - LORIANancyFrance

Personalised recommendations