Query and content suggestion based on latent interest and topic class

Authors:
Noriaki Kawaeme;Hideaki Suzuki;Osamu Mizuno
Affiliations:
NTT Information Sharing Platform Laboratories, Tokyo, Japan;NTT Information Sharing Platform Laboratories, Tokyo, Japan;NTT Information Sharing Platform Laboratories, Tokyo, Japan
Venue:
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Year:
2004

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Abstract

To improve the process of user information retrieval, we propose the concept of a latent semantic map (LSM), along with a method of generating this map. The novel aspect of the LSM is that it can archive user models and latent semantic analysis on one map to support instantaneous information retrieval. With this characteristic, the LSM can improve search engines in terms of not only user support but also search results.