Using Temporal Data for Making Recommendations
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Using Sequential and Non-Sequential Patterns in Predictive Web Usage Mining Tasks
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An MDP-Based Recommender System
The Journal of Machine Learning Research
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Beyond the usual suspects: context-aware revisitation support
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collaborative temporal order modeling
Proceedings of the fifth ACM conference on Recommender systems
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
Data Mining and Knowledge Discovery
Effective next-items recommendation via personalized sequential pattern mining
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Supercharging recommender systems using taxonomies for learning user purchase behavior
Proceedings of the VLDB Endowment
Learning binary codes for collaborative filtering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Playlist prediction via metric embedding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Context-aware document recommendation by mining sequential access data
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
Increasing temporal diversity with purchase intervals
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Local implicit feedback mining for music recommendation
Proceedings of the sixth ACM conference on Recommender systems
Collaborative filtering by analyzing dynamic user interests modeled by taxonomy
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Recommendation in Online Health Communities
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Content recommendation on web portals
Communications of the ACM
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
A hidden Markov model for collaborative filtering
MIS Quarterly
Modeling user's receptiveness over time for recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Opportunity model for e-commerce recommendation: right product; right time
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Multi-space probabilistic sequence modeling
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering meets next check-in location prediction
Proceedings of the 22nd international conference on World Wide Web companion
Personalized recommendation via cross-domain triadic factorization
Proceedings of the 22nd international conference on World Wide Web
Is it time for a career switch?
Proceedings of the 22nd international conference on World Wide Web
Personalized news recommendation with context trees
Proceedings of the 7th ACM conference on Recommender systems
Towards scalable and accurate item-oriented recommendations
Proceedings of the 7th ACM conference on Recommender systems
Personalized next-song recommendation in online karaokes
Proceedings of the 7th ACM conference on Recommender systems
Where you like to go next: successive point-of-interest recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Mining novelty-seeking trait across heterogeneous domains
Proceedings of the 23rd international conference on World wide web
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Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned - thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.