Information retrieval based on collaborative filtering with latent interest semantic map

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. Categories and Subject Descriptors H.3.1 [Analysis and Indexing]: Clustering, Information filtering, MAP, PLSA, Retrieval models General Terms: Documentation Keywords Collaborative Filtering, User be...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Noriaki Kawamae, Katsumi Takahashi
• Keywords: Data Mining, KDD 2005, Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Retrieval Models