Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Informative household recommendation with feature-based matrix factorization
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Learning recommender systems with adaptive regularization
Proceedings of the fifth ACM international conference on Web search and data mining
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Feature based informative model for discriminating favorite items from unrated ones
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Collaborative personalized tweet recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
ACM Transactions on Interactive Intelligent Systems (TiiS)
Scaling factorization machines to relational data
Proceedings of the VLDB Endowment
Exploiting ranking factorization machines for microblog retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
To personalize or not: a risk management perspective
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Celebrity recommendation with collaborative social topic regression
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Predicting response in mobile advertising with hierarchical importance-aware factorization machine
Proceedings of the 7th ACM international conference on Web search and data mining
Improvement quality of the recommendation system using the intrinsic context
Journal of Mobile Multimedia
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In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. In contrast to SVMs, FMs model all interactions between variables using factorized parameters. Thus they are able to estimate interactions even in problems with huge sparsity (like recommender systems) where SVMs fail. We show that the model equation of FMs can be calculated in linear time and thus FMs can be optimized directly. So unlike nonlinear SVMs, a transformation in the dual form is not necessary and the model parameters can be estimated directly without the need of any support vector in the solution. We show the relationship to SVMs and the advantages of FMs for parameter estimation in sparse settings. On the other hand there are many different factorization models like matrix factorization, parallel factor analysis or specialized models like SVD++, PITF or FPMC. The drawback of these models is that they are not applicable for general prediction tasks but work only with special input data. Furthermore their model equations and optimization algorithms are derived individually for each task. We show that FMs can mimic these models just by specifying the input data (i.e. the feature vectors). This makes FMs easily applicable even for users without expert knowledge in factorization models.