Abstract
Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identifying which method dominates the others and under which circumstances. Although some of these methods have been studied independently, no prior research compared the relative performance to determine which of them is better. This paper proposes an effective method of comparing the three methods to incorporate context and selecting the best alternatives. As a result, it provides analysts with a practical suggestion on how to pick a good approach in an effective manner to improve the performance of a context-aware recommender system.
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Notes
For simplicity, we will use the terms “utility” and “rating” interchangeably in this paper, despite the fact that there are certain differences between these two notions.
Note that the MAE and RMSE measures are not applicable to the “top-k” strategy because these measures are calculated on the whole matrix of predicted ratings.
Abbreviations
- RSs:
-
Recommender systems
- CARS:
-
Context-aware recommender systems
- 2D:
-
2-dimensional
- PreF:
-
Contextual pre-filtering
- PoF:
-
Contextual post-filtering
- CM:
-
Contextual modeling
- CTRs:
-
Click-through rates
- EPF:
-
Exact pre-filtering;
- CF:
-
Collaborative filtering
- MAE:
-
Mean absolute error
- RMSE:
-
Root mean square error
References
Bettman, J., Luce, M., & Payne, J. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187–217.
Palmisano, C., Tuzhilin, A., & Gorgoglione, M. (2008). Using context to improve predictive modeling of customers in personalization applications. IEEE Transactions on Knowledge and Data Engineering, 20(11), 1535–1549.
Bettman, J. R., Luce, M. F., & Payne, J. W. (1991). Consumer decision making: a constructive perspective (Consumer behavior and decision making).
Lussier, J. G., & Olshavsky, R. W. (1979). Task complexity and contingent processing in brand choice. Journal of Consumer Research, 6(2), 154–165.
Lilien, G. L., Kotler, P., & Moorthy, S. K. (1992). Marketing models. New York: Prentice Hall.
Cena, F., Console, L., Gena, C., Goy, A., Levi, G., Modeo, S., et al. (2006). Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Communications, 19(4), 369–384.
van Setten, M., Pokraev, S., & Koolwaaij, J. (2004). Context-aware recommendations in the mobile tourist application COMPASS. In Adaptive hypermedia and adaptive web-based systems (pp. 235–244).
Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23, 103–145.
Adomavicius, G., & Tuzhilin, A. (2001). Multidimensional recommender systems: A data warehousing approach. In Electronic commerce (pp. 180–192).
Anand, S., Mobasher, B., Berendt, B., Hotho, A., Mladenic, D., & Semeraro, G. (2007). Contextual recommendation. In From web to social web: Discovering and deploying user and content profiles (Vol. 4737, pp. 142–160). Berlin: Springer.
Oku, K., Nakajima, S., Miyazaki, J., & Uemura, S. (2006). Context-aware SVM for context-dependent information recommendation. In MDM ’06: Proceedings of the 7th international conference on mobile data management (MDM’06). Washington: IEEE Computer Society.
Yu, Z., Zhou, X., Zhang, D., Chin, C.-Y., Wang, X., & Men, J. (2006). Supporting context-aware media recommendations for smart phones. IEEE Pervasive Computing, 5(3), 68–75.
Herlocker, J., & Konstan, J. (2001). Content-independent task-focused recommendation. IEEE Internet Computing, 5(6), 40–47.
Adomavicius, G., & Tuzhilin, A. (2010). Context-aware recommender systems. In Handbook on recommender systems. Berlin: Springer.
Chen, L. S., Hsu, F. H., Chen, M. C., & Hsu, Y. C. (2008). Developing recommender systems with the consideration of product profitability for sellers. Information Sciences, 178(4), 1032–1048.
Huang, Z., Chung, W., & Chen, H. (2004). A graph model for e-commerce recommender systems. Journal of the American Society for Information Science and Technology, 55, 259–274.
Huang, Z., Li, X., & Chen, H. (2005). Link prediction approach to collaborative filtering. In JCDL ’05: Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries (pp. 141–142). New York: ACM Press.
Kwon, O., & Kim, J. (2009). Concept lattices for visualizing and generating user profiles for context-aware service recommendations. Expert Systems with Applications, 36(2), 1893–1902.
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58. doi:10.1145/245108.245121.
Breese, J., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th conference on uncertainty in artificial intelligence (Vol. 461, pp. 43–52).
Kachigan, S. K. (1986). Statistical analysis. New York: Radius Press.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53. doi:10.1145/963770.963772.
Nichols, D. M. (1998). Implicit rating and filtering. In Proceedings of the fifth DELOS workshop on filtering and collaborative filtering (pp. 31–36).
Oard, D., & Kim, J. (1998). Implicit feedback for recommender systems. In Proceedings of the AAAI workshop on recommender systems (pp. 81–83).
Adomavicius, G., Huang, Z., & Tuzhilin, A. (2008). Personalization and recommender systems. In Z.-L. C. a. S. Raghavan (Ed.), State-of-the-art decision making tools in the information-intensive age (pp. 55–100). Tutorials in Operations Research.
Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., & Pedone, A. (2009). Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In RecSys ’09: Proceedings of the third ACM conference on recommender systems, New York, USA (pp. 265–268). New York: ACM.
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Panniello, U., Gorgoglione, M. Incorporating context into recommender systems: an empirical comparison of context-based approaches. Electron Commer Res 12, 1–30 (2012). https://doi.org/10.1007/s10660-012-9087-7
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DOI: https://doi.org/10.1007/s10660-012-9087-7