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Latent semantic analysis for multiple-type interrelated data objects

Xuanhui Wang, Jian-Tao Sun, Zheng Chen, and ChengXiang Zhai

Abstract

Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.

Details

Publication typeInproceedings
Published inSIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
URLhttp://doi.acm.org/10.1145/1148170.1148214
Pages236–243
ISBN1-59593-369-7
AddressNew York, NY, USA
PublisherACM
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