Abstract
Given a set of objects and a set of user preferences, both defined over a set of categorical attributes, the Multiple Categorical Preferences (MCP) problem is to determine the objects that are considered preferable by all users. In a naïve interpretation of MCP, matching degrees between objects and users are aggregated into a single score which ranks objects. Such an approach, though, obscures and blurs individual preferences, and can be unfair, favoring users with precise preferences and objects with detailed descriptions. Instead, we propose an objective and fair interpretation of the MCP problem, based on two Pareto-based aggregations. We introduce an efficient approach that is based on a transformation of the categorical attribute values and an index structure. Moreover, we propose an extension for controlling the number of returned objects. An experimental study on real and synthetic data finds that our index-based technique is an order of magnitude faster than a baseline approach, scaling up to millions of objects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Borgida, A., Jagadish, H.V.: Efficient management of transitive relationships in large data and knowledge bases. In: SIGMOD (1989)
Agrawal, R., Wimmers, E.L.: A framework for expressing and combining preferences. In: SIGMOD (2000)
Aslam, J.A., Montague, M.H.: Models for metasearch. In: SIGIR (2001)
Bartolini, I., Ciaccia, P., Patella, M.: Efficient Sort-based Skyline Evaluation. TODS 33(4) (2008)
Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The r*-tree: An efficient and robust access method for points and rectangles. In: SIGMOD (1990)
Bikakis, N., Benouaret, K., Sacharidis, D.: Reconciling multiple categorical preferences with double pareto-based aggregation. Technical Report (2013), http://www.dblab.ntua.gr/~bikakis/MCP.pdf
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46 (2013)
Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE (2001)
Chan, C.Y., Eng, P.-K., Tan, K.-L.: Stratified computation of skylines with partiallyordered domains. In: SIGMOD (2005)
Chan, C.Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: Finding k-dominant skylines in high dimensional space. In: SIGMOD (2006)
Chen, L., Lian, X.: Efficient processing of metric skyline queries. TKDE 21(3) (2009)
Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: ICDE (2003)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: WWW (2001)
Godfrey, P., Shipley, R., Gryz, J.: Algorithms and analyses for maximal vector computation. VLDBJ 16(1) (2007)
Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007)
Kießling, W.: Foundations of preferences in database systems. In: VLDB (2002)
Kung, H.T., Luccio, F., Preparata, F.P.: On finding the maxima of a set of vectors. Journal of the ACM 22(4) (1975)
Lacroix, M., Lavency, P.: Preferences:Putting more knowledge into queries. In: VLDB (1987)
Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: The k most representative skyline operator. In: ICDE (2007)
Lofi, C., Balke, W.-T.: On skyline queries and how to choose from pareto sets. In: Catania, B., Jain, L.C. (eds.) Advanced Query Processing. ISRL, vol. 36, pp. 15–36. Springer, Heidelberg (2012)
Ntoutsi, E., Stefanidis, K., Nørvåg, K., Kriegel, H.-P.: Fast group recommendations by applying user clustering. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 126–140. Springer, Heidelberg (2012)
P, D., Deshpande, P.M., Majumdar, D., Krishnapuram, R.: Efficient skyline retrieval with arbitrary similarity measures. In: EDBT (2009)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. TODS 30(1) (2005)
Roy, S.B., Amer-Yahia, S., Chawla, A., Das, G., Yu, C.: Space efficiency in group recommendation. VLDB J. 19(6) (2010)
Sacharidis, D., Papadopoulos, S., Papadias, D.: Topologically sorted skylines for partially ordered domains. In: ICDE (2009)
Stefanidis, K., Koutrika, G., Pitoura, E.: A survey on representation, composition and application of preferences in database systems. TODS 36(3) (2011)
Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: ICDE (2009)
Wong, R.C.-W., Fu, A.W.-C., Pei, J., Ho, Y.S., Wong, T., Liu, Y.: Efficient skyline querying with variable user preferences on nominal attributes. VLDB 1(1) (2008)
Yiu, M.L., Mamoulis, N.: Efficient processing of top-k dominating queries on multidimensional data. In: VLDB (2007)
Zhang, S., Mamoulis, N., Kao, B., Cheung, D.W.-L.: Efficient skyline evaluation over partially ordered domains. VLDB 3(1) (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bikakis, N., Benouaret, K., Sacharidis, D. (2014). Reconciling Multiple Categorical Preferences with Double Pareto-Based Aggregation. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8421. Springer, Cham. https://doi.org/10.1007/978-3-319-05810-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-05810-8_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05809-2
Online ISBN: 978-3-319-05810-8
eBook Packages: Computer ScienceComputer Science (R0)