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Author Sangkyum Kim ♦ Jaebum Kim ♦ Younhee Ko ♦ Seung Won Hwang ♦ Jiawei Han
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Algorithm design and analysis ♦ Support vector machines ♦ Personalization ♦ Ranking ♦ Clustering algorithms ♦ Categorical Attributes ♦ Machine learning ♦ Numerical models ♦ Classification algorithms ♦ Construction industry
Abstract Ranking has been popularly used for intelligent data retrieval in both database and machine learning communities. Recently, there were studies on integrating these two approaches to support soft queries, based on a user's sense of relevance and preference, for ranking with numerical attributes. However, in real life, it is desirable to use categorical attributes together with numerical ones in ranking. For example, when buying a car, categorical attributes, such as make, model, color, and equipments, are considered as significant factors as numerical attributes, such as price and year. Meanwhile, users often do not have sufficient domain knowledge at formulating an effective selection query over categories, whereas rank formulation is even more challenging as categories have no inherent ordering. In this paper, we propose a framework PerRank (Personalized Ranking with Categorical and Numerical Attributes) to support personalized ranking with both categorical and numerical attributes for soft queries. For an efficient computation, we developed an algorithm CAC (Clustering-based Attribute Construction) which makes use of a clustering method. Extensive experiments show CAC is effective and efficient at supporting ranking with both categorical and numerical attributes for soft queries.
Description Author affiliation: Comput. Sci. Dept., Univ. of Illinois at Urbana-Champaign, Urbana, IL (Sangkyum Kim; Jaebum Kim; Younhee Ko; Seung Won Hwang)
ISBN 9780769531854
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-07-20
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 281.45 kB
Page Count 8
Starting Page 270
Ending Page 277

Source: IEEE Xplore Digital Library