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Designing a Context Dependent Movie Recommender: A Hierarchical Bayesian Approach
| Content Provider | CiteSeerX |
|---|---|
| Author | Pomerantz, Daniel |
| Abstract | c○Daniel Pomerantz, 2009ACKNOWLEDGEMENTS I would like to thank everyone that helped during my graduate studies. I want to thank my supervisor Gregory Dudek for all his support, encouragement, and general kindness. I’d also like to thank all the members of the Mobile Robotics Lab who were very helpful whenever I had a problem, be it math, computers, or otherwise. Finally, I’d like to thank my family and friends for all their support throughout my graduate studies and life. In this thesis, we analyze a context-dependent movie recommendation system using a Hierarchical Bayesian Network. Unlike most other recommender systems which either do not consider context or do so using collaborative filtering, our approach is content-based. This allows users to individually interpret contexts or invent their own contexts and continue to get good recommendations. By using a Hierarchical Bayesian Network, we can provide context recommendations when users have only provided a small amount of information about their preferences per context. At the |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Context Dependent Movie Recommender Small Amount Collaborative Filtering General Kindness Mobile Robotics Lab Hierarchical Bayesian Network Context-dependent Movie Recommendation System Supervisor Gregory Dudek Helpful Whenever Context Recommendation Graduate Study Good Recommendation Hierarchical Bayesian Approach Recommender System |
| Content Type | Text |
