Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
A prediction system for multimedia pre-fetching in Internet
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Using Temporal Data for Making Recommendations
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Maximum entropy for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Collaborative Filtering with Maximum Entropy
IEEE Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Advice generation from observed execution: abstract Markov decision process learning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Recsplorer: recommendation algorithms based on precedence mining
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Proceedings of the 15th International Conference on Extending Database Technology
Assessing the appropriateness of using markov decision processes for RF spectrum management
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
Mining novelty-seeking trait across heterogeneous domains
Proceedings of the 23rd international conference on World wide web
Hi-index | 0.00 |
Typical Recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDP) provide a more appropriate model for Recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation, and they take into account the expected value of each recommendation. To succeed in practice, an MDP-based Recommender system must employ a strong initial model; and the bulk of this paper is concerned with the generation of such a model. In particular, we suggest the use of an n-gram predictive model for generating the initial MDP. Our n-gram model induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models. We describe our predictive model in detail and evaluate its performance on real data. In addition, we show how the model can be used in an MDP-based Recommender system.