{"type":"ScholarlyArticle","title":"Latent Class Models for Collaborative Filtering","authors":[{"name":"Thomas Hofmann","slug":"thomas-hofmann","given":"Thomas","family":"Hofmann","affiliation":""},{"name":"Jan Puzicha","slug":"jan-puzicha","given":"Jan","family":"Puzicha","affiliation":""}],"abstract":"This paper presents a statistical approach to collaborative filtering and investigates the use of lat e nt class mod els for predicting individual choices and preferences based on observed preference behavior . Two models are discussed and compared: the aspect model, a probabilistic latent space model which models individual preferences as a convex combination of preference factors , and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algorithms for different variants of the aspect model and derive an approximate EM algorithm based on a variational principle for the two-sided clustering model. The benefits of the different models are experimentally investigated on a large movie data set.","doi":null,"url":"https://ijcai.org/proceedings/1999/98","pdf":"https://ijcai.org/proceedings/1999/0098.pdf","markdown":"https://ijcai.org/proceedings/1999/98.md","bibtex":"https://ijcai.org/proceedings/1999/bibtex/98","volume":{"slug":"1999","title":"Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1999","url":"https://ijcai.org/proceedings/1999/","year":1999,"location":"Stockholm, Sweden","dates":"","editors":"Thomas Dean","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":98,"pages":{"first":688,"last":692},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}