Abstract
In the retailing industry, recommendation systems analyze historical purchasing information with the purpose of predicting user product preferences. Nevertheless, despite the increasing use of these applications, their results still lack precision with respect to the real needs and preferences of customers. This is in part because the user’s purchase history is insufficient to identify the products that a user would need to buy, given that user preferences are highly affected by changes in contextual situations (e.g., geographical location, special dates, activities of interest) over time. This paper presents a recommendation system that exploits context information to improve the precision of recommendations. Our system relies on the collaborative filtering approach, and the post-filtering paradigm as the mechanism to include context information into the recommendation algorithm. We tested our system using data provided by a Colombian retailing company finding that our recommendations are successful for a greater number of customers, compared to the baseline approach.
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References
Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999). doi:10.1007/3-540-48157-5_29
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. (TOIS) 23(1), 103–145 (2005)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston (2011). doi:10.1007/978-0-387-85820-3_7
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2014)
Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User Adap. Inter. 24(1–2), 67–119 (2014)
Ebrahimi, S., Villegas, N.M., Müller, H.A., Thomo, A.: SmarterDeals: a context-aware deal recommendation system based on the smartercontext engine. In: Proceedings of 2012 Conference of the Center for Advanced Studies on Collaborative Research, pp. 116–130. IBM Corporation (2012)
Fournier-Viger, P., Wu, C.W., Tseng, V.S.: Mining top-k association rules. In: Advances in Artificial Intelligence, pp. 61–73. Springer (2012)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Hong, W., Li, L., Li, T.: Product recommendation with temporal dynamics. Expert Syst. Appl. 39(16), 12398–12406 (2012)
Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. Technical report, Keele University (2007)
Panniello, U., Gorgoglione, M.: Incorporating context into recommender systems: an empirical comparison of context-based approaches. Electron. Commer. Res. 12(1), 1–30 (2012)
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, Heidelberg (2011)
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., Oliver, N.: TFMAP: optimizing map for top-n context-aware recommendation. In: Proceedings of 35th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 155–164. ACM (2012)
Tan, P.N., Steinbach, M., Kumar, V., et al.: Introduction to Data Mining, vol. 1. Pearson Addison Wesley, Boston (2006)
Villegas, N.M.: Context Management and Self-Adaptivity for Situation-Aware Smart Software Systems. Ph.D. thesis, University of Victoria (2013)
Villegas, N.M., Müller, H.A.: Managing dynamic context to optimize smart interactions and services. In: Chignell, M., Cordy, J., Ng, J., Yesha, Y. (eds.) The Smart Internet. LNCS, vol. 6400, pp. 289–318. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16599-3_18
Zheng, C., Haihong, E., Song, M., Song, J.: CMPTF: contextual modeling probabilistic tensor factorization for recommender systems. Neurocomputing 205, 141–151 (2016)
Zou, B., Li, C., Tan, L., Chen, H.: GPUTENSOR: efficient tensor factorization for context-aware recommendations. Inf. Sci. 299, 159–177 (2015)
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Sánchez, C., Villegas, N.M., Díaz Cely, J. (2017). Exploiting Context Information to Improve the Precision of Recommendation Systems in Retailing. In: Solano, A., Ordoñez, H. (eds) Advances in Computing. CCC 2017. Communications in Computer and Information Science, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-319-66562-7_6
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