LAUSR

Collaborative filtering usually suffers from limited performance due to the data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue through transfer useful knowledge from an auxiliary domain to a target domain. Cluster-level rating patterns transformation models have been widely used due to the loose restriction which does not assume the source overlaps users and items with the target. However, previous researches have never investigated the relationship between the codebook scale in transfer learning and the prediction accuracy in the target domain. Moreover, all existing rating patterns sharing models fix the codebook scale without considering the data features of the source domain. In this paper, we propose a novel model, namely ACTL, to efficiently and automatically discover the appropriate codebook scale, which balances both the computational cost and prediction accuracy and best matches the size and features of the source domain for the cross-domain recommendation. The extensive experiments on real-world datasets demonstrate that our algorithms get knowledge gain from the large source domain and clearly and solidly outperform the state-of-the-art fixed scale codebook transfer learning methods.