MatchSim: a novel neighbor-based similarity measure with maximum neighborhood matching

The problem of measuring similarity between web pages arises in many important Web applications, such as search engines and Web directories. In this paper, we propose a novel neighbor-based similarity measure called MatchSim, which uses only the neighborhood structure of web pages. Technically, MatchSim recursively defines similarity between web pages by the average similarity of the maximum matching between their neighbors. Our method extends the traditional methods which simply count the numbers of common and/or different neighbors. It also successfully overcomes a severe counterintuitive loophole in SimRank, due to its strict consistency with the intuitions of similarity. We give the computational complexity of MatchSim iteration. The accuracy of MatchSim is compared with others on two real datasets. The results show that the method performs best in most cases. Categories and Subject Descriptors: H.3.3 Information Search and Retrieval: Clustering; Information filtering General T...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Zhenjiang Lin, Michael R. Lyu, Irwin King
• Keywords: Average Similarity, CIKM 2009, Database, Neighbor-based Similarity Measure, Web Pages