%0 Conference Paper %1 Hofmann2003 %A Hofmann, Thomas %B Special issue of the SIGIR forum %C New York, NY %D 2003 %E Callan, Jamie %I ACM Press %K analysis collaborative filtering latent probabilistic semantic %T Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis %U http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR2003.pdf %V 2003 %X Collaborative Filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. More specically, we assume that the observed user ratings can be modeled as a mixture of user communities or interest groups, where users may participate probabilistically in one or more groups. Each community is characterized by a Gaussian distribution on the normalized ratings for each item. The normalization of ratings is performed in a user-specific manner to account for variations in absolute shift and variance of ratings. Experiments on the Each-Movie data set show that the proposed approach compares favorably with other collaborative Filtering techniques. %@ 1581136463

@inproceedings{Hofmann2003, abstract = {Collaborative Filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. More specically, we assume that the observed user ratings can be modeled as a mixture of user communities or interest groups, where users may participate probabilistically in one or more groups. Each community is characterized by a Gaussian distribution on the normalized ratings for each item. The normalization of ratings is performed in a user-specific manner to account for variations in absolute shift and variance of ratings. Experiments on the Each-Movie data set show that the proposed approach compares favorably with other collaborative Filtering techniques.}, added-at = {2007-06-28T14:05:08.000+0200}, address = {New York, NY}, author = {Hofmann, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2b524d104b84217ba969065143a05d114/wnpxrz}, crossref = {Callan:2003}, editor = {Callan, Jamie}, interhash = {0cf79cc120e8c7b216ba6bfa708a4602}, intrahash = {b524d104b84217ba969065143a05d114}, isbn = {1581136463}, keywords = {analysis collaborative filtering latent probabilistic semantic}, organization = {International Conference on Research and Development in Information Retrieval}, publisher = {ACM Press}, series = {Special issue of the SIGIR forum}, timestamp = {2007-06-28T14:05:08.000+0200}, title = {Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis}, url = {http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR2003.pdf}, volume = 2003, year = 2003 }