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Probabilistic Reinforcement Rules for Item-Based Recommender Systems
| Content Provider | Hyper Articles en Ligne (HAL) |
|---|---|
| Author | Castagnos, Sylvain Brun, Armelle Boyer, Anne |
| Copyright Year | 2008 |
| Abstract | The Internet is constantly growing, proposing more and more services and sources of information. Modeling personal preferences enables recommender systems to identify relevant subsets of items. These systems often rely on filtering techniques based on symbolic or numerical approaches in a stochastic context. In this paper, we focus on item-based collaborative filtering (CF) techniques. We propose a new approach combining a classic CF algorithm with a reinforcement model to get a better accuracy. We deal with this issue by exploiting probabilistic skewnesses in triplets of items. |
| Related Links | https://inria.hal.science/inria-00329560/file/525-ECAI08-CastagnosBrunBoyer-CameraReady.pdf |
| Conference Proceedings | 18th European Conference on Artificial Intelligence (ECAI 2008) |
| Language | English |
| Publisher | HAL CCSD |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Ranking Recommender Systems Probabilistic reasoning |
| Content Type | Text |
| Resource Type | Conference Proceedings |
| Subject | Mathematics Computer Science |
