Abstract
The success of recommender systems has made them the focus of a massive research effort in both industry and academia. The aim of most recommender systems is to identify a ranked list of items that are likely to be of interest to users. However, there are several applications such as trip planning, where users are interested in package recommendations as collections of items. In this paper, we consider the problem of recommending the top-k packages to the active user, where each package is constituted with a set of points of interest (POIs) and is under user-specified constraints (time, price, etc.). We formally define the problem of top-k composite recommendations and present our approach which is inspired from composite retrieval. We introduce a scoring function and propose a ranking algorithm for solving the top-k packages problem, taking into account the preferences of the user, the diversity of recommended packages as well as the popularity of POIs. An experimental evaluation of our proposal, using data crawled from Tripadvisor demonstrates its quality and its ability to provide relevant and diverse package recommendations.
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Amer-Yahia, S., Bonchi, F., Castillo, C., Feuerstein, E., Méndez-Díaz, I., Zabala, P.: Complexity and algorithms for composite retrieval. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 79–80. ACM (2013)
Amer-Yahia, S., Bonchi, F., Castillo, C., Feuerstein, E., Mendez-Diaz, I., Zabala, P.: Composite retrieval of diverse and complementary bundles. IEEE Trans. Knowl. Data Eng. 26(11), 2662–2675 (2014)
Angel, A., Chaudhuri, S., Das, G., Koudas, N.: Ranking objects based on relationships and fixed associations. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT 2009, pp. 910–921. ACM, New York (2009)
Brodsky, A., Henshaw, S.M., Whittle, J.: Card: a decision-guidance framework and application for recommending composite alternatives. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 171–178. ACM, New York (2008)
Candillier, L., Chevalier, M., Dudognon, D., Mothe, J.: Diversity in recommender systems: bridging the gap between users and systems. Centrics (2011). To appear
Castillo, L., Armengol, E., Onaindía, E., Sebastiá, L., González-Boticario, J., Rodríguez, A., Fernández, S., Arias, J.D., Borrajo, D.: Samap: an user-oriented adaptive system for planning tourist visits. Expert Syst. Appl. 34(2), 1318–1332 (2008)
De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., Yu, C.: Automatic construction of travel itineraries using social breadcrumbs. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, pp. 35–44. ACM (2010)
Hansen, D.L., Golbeck, J.: Mixing it up: recommending collections of items. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1217–1226. ACM (2009)
Parameswaran, A., Venetis, P., Garcia-Molina, H.: A course recommendation perspective. Technical report, Recommendation systems with complex constraints (2009)
Parameswaran, A.G., Garcia-Molina, H.: Recommendations with prerequisites. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 353–356. ACM (2009)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Steck, H.: Item popularity and recommendation accuracy. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 125–132. ACM, New York (2011)
Xie, M., Lakshmanan, L.V.S., Wood, P.T.: Breaking out of the box of recommendations: from items to packages. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 151–158. ACM, New York (2010)
Xie, M., Lakshmanan, L.V.S., Wood, P.T.: Comprec-trip: a composite recommendation system for travel planning. In: 2011 IEEE 27th International Conference on Data Engineering (ICDE), pp. 1352–1355. IEEE (2011)
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)
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Benouaret, I., Lenne, D. (2017). Recommending Diverse and Personalized Travel Packages. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_26
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DOI: https://doi.org/10.1007/978-3-319-64471-4_26
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