Information retrieval based on collaborative filtering with latent interest semantic map

@inproceedings{citeulike:2282032, 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.}, address = {New York, NY, USA}, author = {Kawamae, Noriaki and Takahashi, Katsumi }, booktitle = {KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining}, citeulike-article-id = {2282032}, doi = {10.1145/1081870.1081946}, isbn = {159593135X}, keywords = {adaptive-search, collaborative-filtering, user-model}, pages = {618--623}, posted-at = {2008-01-23 20:13:24}, priority = {2}, publisher = {ACM}, title = {Information retrieval based on collaborative filtering with latent interest semantic map}, url = {http://portal.acm.org/citation.cfm?id=1081946}, year = {2005} }

TY - CONF ID - citeulike:2282032 L3 - citeulike-article-id:2282032 TI - Information retrieval based on collaborative filtering with latent interest semantic map T2 - KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining SP - 618 EP - 623 AD - New York, NY, USA PB - ACM SN - 159593135X N2 - 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. KW - adaptive-search KW - collaborative-filtering KW - user-model AU - Kawamae, Noriaki AU - Takahashi, Katsumi PY - 2005/// UR - http://portal.acm.org/citation.cfm?id=1081946 ER -