Context-Aware Ranking with Factorization Models
Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, ...) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.
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1C-MMMF 1C-PMMMF approach argmax basis functions basket Bayesian BCR-LEARN BCR-OPT BibSonomy binary BPoR BPoR-PITF categorical variables chapter classiﬁcation collaborative ﬁltering Computation context context-aware ranking core tensor Data Mining dataset deﬁned dimensionality Discovery Challenge domains estimator evaluation example factorization dimensions ﬁgure ﬁrst FolkRank FPMC Furthermore HOSVD hyperparameter implicit feedback International Conference item recommendation kernel Last.fm learning algorithm Machine Learning MAP estimator matrix factorization matrix factorization model method model equation model parameters modes Movielens Netﬂix non-personalized npmax observed data one-class optimization criterion outperforms PageRank pairs pairwise interaction PARAFAC model personalized Markov chains PITF model prediction quality Proceedings Ranking with Factorization recommender systems Rendle RTF-TD runtime Schmidt-Thieme sequential singular value decomposition sparse sparsity speciﬁc Srebro stochastic gradient descent tag recommendation task tensor factorization time-variant factor training data transition cube transition matrix Tucker decomposition two-mode update variable instances