  Collaborative filtering (CF) aims to predict users' ratings on items
according to historical user-item preference data. In many real-world
applications, preference data are usually sparse, which would make models
overfit and fail to give accurate predictions. Recently, several research works
show that by transferring knowledge from some manually selected source domains,
the data sparseness problem could be mitigated. However for most cases, parts
of source domain data are not consistent with the observations in the target
domain, which may misguide the target domain model building. In this paper, we
propose a novel criterion based on empirical prediction error and its variance
to better capture the consistency across domains in CF settings. Consequently,
we embed this criterion into a boosting framework to perform selective
knowledge transfer. Comparing to several state-of-the-art methods, we show that
our proposed selective transfer learning framework can significantly improve
the accuracy of rating prediction tasks on several real-world recommendation
tasks.
