Learning From Missing Data Using Selection Bias in Movie Recommendation
Résumé
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.
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