Learning From Missing Data Using Selection Bias in Movie Recommendation
This addresses the issue of unreliable recommendations for users in systems with sparse data, though it is incremental as it builds on existing collaborative filtering methods.
The paper tackled the problem of missing data in movie recommendation by showing that selection bias exists in datasets, and proposed a variational approach to exploit this bias, improving rating estimation from small user populations.
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.