Abstract
In this paper, we propose a skyline-based recommendation and ranking function. We suppose that some recommender systems, such as hotel recommender systems, are based not only on user preferences but also cost performance. For these kinds of applications, We first extract items with good cost performance and then identify items that users prefer, which reduce the computational cost of the online process. Based on the results of our preliminary experiments, we propose user feedback-based scoring and density-aware scoring methods where items that are highly similar to a user’s latent requirements are recommended and attribute values in a dense area are quantized into a single value. The result of the experiments suggest that the density-aware scoring provides equal to or greater accuracy than the basic scoring.
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Notes
- 1.
As k increases, the system may be able to express the user’s latent requirements more precisely. However, a user’s labor increases when the number of seed skyline points increases.
References
Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceeding of the 17th ICDE, pp. 421–430 (2001)
Bosc, P., Hadjali, A., Pivert, O.: On possibilistic skyline queries. In: Christiansen, H., Tré, G., Yazici, A., Zadrozny, S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2011. LNCS (LNAI), vol. 7022, pp. 412–423. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24764-4_36
Bosc, P., Pivert, O.: Fuzzy queries and relational databases. In: Proceeding of SAC, pp. 170–174 (1994)
Bosc, P., Pivert, O.: SQLf: a relational database language for fuzzy querying. IEEE Trans. Fuzzy Syst. 3, 1–17 (1995)
Bosc, P., Pivert, O., Mokhtari, A.: Top-k queries with contextual fuzzy preferences. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2009. LNCS, vol. 5690, pp. 847–854. Springer, Heidelberg (2009). doi:10.1007/978-3-642-03573-9_72
Hadjali, A., Pivert, O., Prade, H.: On different types of fuzzy skylines. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS (LNAI), vol. 6804, pp. 581–591. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21916-0_62
Lee, H.-H., Teng, W.-G.: Incorporating multi-criteria ratings in recommendation systems. In: Proceeding of the IEEE IRI, pp. 273–278 (2007)
Lops, P., de Gemmis, M., Semeraro, G.: Systems, content-based recommender: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, New York (2011). doi:10.1007/978-0-387-85820-3_3
Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: Proceeding of SIGMOD, pp. 467–478 (2003)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceeding of CSCW, pp. 175–184 (1994)
Sacharidis, D., Arvanitis, A., Sellis, T.: Probabilistic contextual skylines. In: Proceeding of the 26th ICDE, pp. 273–284 (2010)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceeding of the 10th WWW, pp. 285–295 (2001)
Silverman, W.B.: Density Estimation for Statistics and Data Analysis. CRC Press, London (1986)
Yiu, M.L., Mamoulis, N.: Efficient processing of top-k dominating queries on multi-dimensional data. In: Proceeding of the 33rd VLDB, pp. 483–494 (2007)
Acknowledgements
This work was partly supported by JSPS KAKENHI Grant Numbers 15H02701, 15K20990, 16H02908, 26280115, and 25240014. The Rakuten Travel data set was provided according to the contract between NII and Rakuten, Inc.
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Kishida, S., Ueda, S., Keyaki, A., Miyazaki, J. (2017). Skyline-Based Recommendation Considering User Preferences. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_11
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