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Combining Node Identifier Features and Community Priors for Within-Network Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Skyline-Based Recommendation Considering User Preferences

Authors : Shuhei Kishida, Seiji Ueda, Atsushi Keyaki, Jun Miyazaki

Published in: Web and Big Data

Publisher: Springer International Publishing

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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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previous chapter Learning Intermediary Category Labels for Personal Recommendation
next chapter Improving Topic Diversity in Recommendation Lists: Marginally or Proportionally?
Footnotes
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.
 
Literature
1.
Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceeding of the 17th ICDE, pp. 421–430 (2001)
2.
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 CrossRef
3.
Bosc, P., Pivert, O.: Fuzzy queries and relational databases. In: Proceeding of SAC, pp. 170–174 (1994)
4.
Bosc, P., Pivert, O.: SQLf: a relational database language for fuzzy querying. IEEE Trans. Fuzzy Syst. 3, 1–17 (1995)CrossRef
5.
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 CrossRef
6.
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 CrossRef
7.
Lee, H.-H., Teng, W.-G.: Incorporating multi-criteria ratings in recommendation systems. In: Proceeding of the IEEE IRI, pp. 273–278 (2007)
8.
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 CrossRef
9.
Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: Proceeding of SIGMOD, pp. 467–478 (2003)
10.
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)
11.
Sacharidis, D., Arvanitis, A., Sellis, T.: Probabilistic contextual skylines. In: Proceeding of the 26th ICDE, pp. 273–284 (2010)
12.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceeding of the 10th WWW, pp. 285–295 (2001)
13.
Silverman, W.B.: Density Estimation for Statistics and Data Analysis. CRC Press, London (1986)CrossRefMATH
14.
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)
Metadata
Title
Skyline-Based Recommendation Considering User Preferences
Authors
Shuhei Kishida
Seiji Ueda
Atsushi Keyaki
Jun Miyazaki
Copyright Year
2017
Book
Web and Big Data

Print ISBN: 978-3-319-63563-7

Electronic ISBN: 978-3-319-63564-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63564-4_11

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