Abstract
There are a large amount of active Web users whose behaviors reveal their preferences. The user preference model can be established via analyzing user behaviors. Mining common preference group (CPG) from the user preference model can support many applications such as potential community discovery, data sharing and recommendation. However, the user preference model is constantly evolving, which is caused by users’ various behaviors. With massive users, each time the user preference model changed, mining CPG from the scratch is not an option. In this paper, we analyze how users’ behaviors influence CPG. Then, an approximate approach for batch CPG update is proposed to avoid CPG re-computing from sketch when the user preference model changed.
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
Jia, D., Zeng, C., Nie, W., Li, Z., Peng, Z.: A New Approach for Date Sharing and Recommendation in Social Web. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012, Part II. LNCS, vol. 7447, pp. 314–328. Springer, Heidelberg (2012)
Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: Proceedings of the Twelfth International Conference on Data Engineering (ICDE 1996), pp. 106–114. IEEE Computer Society (1996)
Cheung, D.W., Lee, S.D., Kao, B.: A general incremental technique for maintaining discovered association rules. In: Topor, R.W., Tanaka, K. (eds.) DASDAA 1997. LNCS, vol. 6, pp. 185–194. Springer, Heidelberg (1997)
Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An efficient algorithm for the incremental updation of association rules in large databases. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD 1997), pp. 263–266. AAAI Press (1997)
Lee, C., Lin, C., Chen, M.: Sliding-window filtering: an efficient algorithm for incremental mining. In: Proceedings of the Tenth International Conference on Information and Knowledge Management (CIKM 2001), pp. 263–270. ACM Press (2001)
Ayan, N.F., Tansel, A.U., Arkun, E.: An efficient algorithm to update large itemsets with early pruning. In. Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD 1999), pp. 287–291. AAAI Press (1999)
Ezeife, C.I., Su, Y.: Mining incremental association rules with generalized FP-tree. In: Cohen, R., Spencer, B. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2338, pp. 147–160. Springer, Heidelberg (2002)
Otey, M., Veloso, A., Wang, C., Parthasarathy, S., Meira, W.: Mining frequent itemsets in distributed and dynamic databases. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), pp. 617–620. IEEE Computer Society (2003)
Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Moment: maintaining closed frequent itemsets over a stream sliding window. In: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM 2004), pp. 59–66. IEEE Computer Society (2004)
Bernhard, G., Rudolf, W.: Formal Concept Analysis: Mathematical Foundations. Springer-Verlag New York, Inc. (1999)
Peng, X., Zhao, W.: An Incremental and FCA-Based Ontology Construction Method for Semantics-Based Component Retrieval. In: Proceedings of the Seventh International Conference on Quality Software (QSIC 2007), pp. 309–315. IEEE Computer Society (2007)
Maddouri, M.: Towards a machine learning approach based on incremental concept formation. Journal of Intelligent Data Analysis 8(3), 267–280 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jia, D., Peng, Z., Zeng, C., Cao, D. (2013). An Approximate Approach for Batch Update of Common Preference Group. In: Hong, B., Meng, X., Chen, L., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40270-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-40270-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40269-2
Online ISBN: 978-3-642-40270-8
eBook Packages: Computer ScienceComputer Science (R0)