Name
Abstract | ||
|---|---|---|
Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current approaches to recommendation usually assume that the unobserved data is missing at random. In this contribution, we provide statistical evidence that existing movie recommendation datasets reveal a significant positive association between the rating of items and the propensity to select these items. We propose a computationally efficient variational approach that makes it possible to exploit this selection bias so as to improve the estimation of ratings from small populations of users. Results obtained with this approach applied to neighborhood-based collaborative filtering illustrate its potential for improving the reliability of the recommendation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DSAA.2015.7344803 | 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) |
Keywords | Field | DocType |
missing data,selection bias,missing information,unobserved data,movie recommendation datasets,positive association,neighborhood-based collaborative filtering | Collaborative filtering,Information retrieval,Computer science,Exploit,Artificial intelligence,Missing data,Selection bias,Machine learning | Journal |
Volume | ISBN | Citations |
abs/1509.09130 | 978-1-4673-8272-4 | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claire Vernade | 1 | 0 | 0.34 |
| O. Cappe | 2 | 2112 | 207.95 |