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| Content Provider | ACM Digital Library |
|---|---|
| Author | Crestani, Fabio Harvey, Morgan Ruthven, Ian Carman, Mark J. |
| Abstract | Collaborative filtering systems based on ratings make it easier for users to find content of interest on the Web and as such they constitute an area of much research. In this paper we first present a Bayesian latent variable model for rating prediction that models ratings over each user's latent interests and also each item's latent topics. We describe a Gibbs sampling procedure that can be used to estimate its parameters and show by experiment that it is competitive with the gradient descent SVD methods commonly used in state-of-the-art systems. We then proceed to make an important and novel extension to this model, enhancing it with user-dependent and item-dependant biases to significantly improve rating estimation. We show by experiment on a large set of real ratings data that these models are able to outperform 3 common baselines, including a very competitive and modern SVD-based model. Furthermore we illustrate other advantages of our approach beyond simply its ability to provide more accurate ratings and show that it is able to perform better on the common and important case where the user profile is short. |
| Starting Page | 699 |
| Ending Page | 708 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450307178 |
| DOI | 10.1145/2063576.2063680 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-10-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | User modelling Topic models Recommender systems |
| Content Type | Text |
| Resource Type | Article |
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