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Information retrieval based on collaborative filtering with latent interest semantic map

Published: 21 August 2005 Publication History

Abstract

In this paper, we propose an information retrieval model called Latent Interest Semantic Map (LISM), which features retrieval composed of both Collaborative Filtering(CF) and Probabilistic Latent Semantic Analysis (PLSA). The motivation behind this study is that the relation between users and documents can be explained by the two different latent classes, where users belong probabilistically in one or more classes with the same interest groups, while documents also belong probabilistically in one or more class with the same topic groups. The novel aspect of LISM is that it simultaneously provides a user model and latent semantic analysis in one map. This benefit of LISM is to enable collaborative filtering in terms of user interest and document topic and thus solve the cold start problem.

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Cited By

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  • (2011)Predicting future reviewsProceedings of the fourth ACM international conference on Web search and data mining10.1145/1935826.1935911(605-614)Online publication date: 9-Feb-2011
  • (2009)Web Usage Mining with Web LogsEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch321(2096-2102)Online publication date: 2009
  • (2007)Adapting Ratings in Memory-Based Collaborative Filtering using Linear Regression2007 IEEE International Conference on Information Reuse and Integration10.1109/IRI.2007.4296596(49-54)Online publication date: Aug-2007

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cover image ACM Conferences
KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
August 2005
844 pages
ISBN:159593135X
DOI:10.1145/1081870
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 August 2005

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Author Tags

  1. Bayesian statistics
  2. collaborative filtering
  3. document categorization
  4. latent semantic indexing
  5. query suggestion
  6. relationship analysis
  7. search results
  8. user behavior

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2011)Predicting future reviewsProceedings of the fourth ACM international conference on Web search and data mining10.1145/1935826.1935911(605-614)Online publication date: 9-Feb-2011
  • (2009)Web Usage Mining with Web LogsEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch321(2096-2102)Online publication date: 2009
  • (2007)Adapting Ratings in Memory-Based Collaborative Filtering using Linear Regression2007 IEEE International Conference on Information Reuse and Integration10.1109/IRI.2007.4296596(49-54)Online publication date: Aug-2007

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