Recommendation with Differential Context Weighting

  • Yong Zheng
  • Robin Burke
  • Bamshad Mobasher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7899)

Abstract

Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach – differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.

Keywords

recommender systems collaborative filtering context context-aware recommendation 

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References

  1. 1.
    Abdelwahab, A., Sekiya, H., Matsuba, I., Horiuchi, Y., Kuroiwa, S.: Feature optimization approach for improving the collaborative filtering performance using particle swarm optimization. Journal of Computational Information Systems 8(1), 435–450 (2012)Google Scholar
  2. 2.
    Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)Google Scholar
  3. 3.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS) 23(1), 103–145 (2005)CrossRefGoogle Scholar
  4. 4.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253 (2011)Google Scholar
  5. 5.
    Tasič, J.F., Košir, A., Odic, A., Tkalcic, M.: Relevant context in a movie recommender system: Users opinion vs. statistical detection. In: ACM RecSys 2012, Proceedings of the 4th International Workshop on Context-Aware Recommender Systems (CARS 2012). ACM (2012)Google Scholar
  6. 6.
    Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing, 1–20 (2011)Google Scholar
  7. 7.
    Bourke, S., McCarthy, K., Smyth, B.: The social camera: a case-study in contextual image recommendation. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, pp. 13–22. ACM (2011)Google Scholar
  8. 8.
    Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  9. 9.
    De Pessemier, T., Deryckere, T., Martens, L.: Extending the bayesian classifier to a context-aware recommender system for mobile devices. In: 2010 Fifth International Conference on Internet and Web Applications and Services (ICIW), pp. 242–247. IEEE (2010)Google Scholar
  10. 10.
    Diaz-Aviles, E., Georgescu, M., Nejdl, W.: Swarming to rank for recommender systems (2012)Google Scholar
  11. 11.
    Diaz-Aviles, E., Nejdl, W., Schmidt-Thieme, L.: Swarming to rank for information retrieval. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 9–16. ACM (2009)Google Scholar
  12. 12.
    Drias, H.: Web information retrieval using particle swarm optimization based approaches. In: 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 36–39. IEEE (2011)Google Scholar
  13. 13.
    Gantner, Z., Rendle, S., Schmidt-Thieme, L.: Factorization models for context-/time-aware movie recommendations. In: Proceedings of the Workshop on Context-Aware Movie Recommendation, pp. 14–19. ACM (2010)Google Scholar
  14. 14.
    Hariri, N., Mobasher, B., Burke, R., Zheng, Y.: Context-aware recommendation based on review mining. In: Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2011), p. 30 (2011)Google Scholar
  15. 15.
    Huang, Z., Lu, X., Duan, H.: Context-aware recommendation using rough set model and collaborative filtering. Artificial Intelligence Review, 1–15 (2011)Google Scholar
  16. 16.
    Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 79–86. ACM (2010)Google Scholar
  17. 17.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)Google Scholar
  18. 18.
    Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997)Google Scholar
  19. 19.
    Khanesar, M., Teshnehlab, M., Shoorehdeli, M.: A novel binary particle swarm optimization. In: Mediterranean Conference on Control & Automation, MED 2007, pp. 1–6. IEEE (2007)Google Scholar
  20. 20.
    Lee, J.S., Lee, J.C.: Context awareness by case-based reasoning in a music recommendation system. In: Ichikawa, H., Cho, W.-D., Satoh, I., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 45–58. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Liang, J., Qin, A., Suganthan, P., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)CrossRefGoogle Scholar
  22. 22.
    Liu, L., Lecue, F., Mehandjiev, N., Xu, L.: Using context similarity for service recommendation. In: 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), pp. 277–284. IEEE (2010)Google Scholar
  23. 23.
    Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Transactions on Evolutionary Computation 8(3), 204–210 (2004)CrossRefGoogle Scholar
  24. 24.
    Ono, C., Takishima, Y., Motomura, Y., Asoh, H.: Context-aware preference model based on a study of difference between real and supposed situation data. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 102–113. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  25. 25.
    Park, H.-S., Yoo, J.-O., Cho, S.-B.: A context-aware music recommendation system using fuzzy bayesian networks with utility theory. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 970–979. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  26. 26.
    Ujjin, S., Bentley, P.: Particle swarm optimization recommender system. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003, pp. 124–131. IEEE (2003)Google Scholar
  27. 27.
    Vargas-Govea, B., González-Serna, G., Ponce-Medellín, R.: Effects of relevant contextual features in the performance of a restaurant recommender system. In: ACM RecSys 2011, The 3rd Workshop on Context-Aware Recommender Systems, CARS-2011 (2011)Google Scholar
  28. 28.
    Woerndl, W., Huebner, J., Bader, R., Gallego-Vico, D.: A model for proactivity in mobile, context-aware recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 273–276. ACM (2011)Google Scholar
  29. 29.
    Zheng, Y., Burke, R., Mobasher, B.: Differential context relaxation for context-aware travel recommendation. In: Huemer, C., Lops, P. (eds.) EC-Web 2012. LNBIP, vol. 123, pp. 88–99. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  30. 30.
    Zheng, Y., Burke, R., Mobasher, B.: Optimal feature selection for context-aware recommendation using differential relaxation. In: ACM RecSys 2012, Proceedings of the 4th International Workshop on Context-Aware Recommender Systems (CARS 2012). ACM (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yong Zheng
    • 1
  • Robin Burke
    • 1
  • Bamshad Mobasher
    • 1
  1. 1.Center for Web Intelligence, School of ComputingDePaul UniversityChicagoUSA

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