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.
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References
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)
Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)
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)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253 (2011)
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)
Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing, 1–20 (2011)
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)
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)
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)
Diaz-Aviles, E., Georgescu, M., Nejdl, W.: Swarming to rank for recommender systems (2012)
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)
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)
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)
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)
Huang, Z., Lu, X., Duan, H.: Context-aware recommendation using rough set model and collaborative filtering. Artificial Intelligence Review, 1–15 (2011)
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)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
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)
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)
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)
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)
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)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Transactions on Evolutionary Computation 8(3), 204–210 (2004)
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)
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)
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)
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)
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)
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)
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)
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Zheng, Y., Burke, R., Mobasher, B. (2013). Recommendation with Differential Context Weighting. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_13
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DOI: https://doi.org/10.1007/978-3-642-38844-6_13
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