PDF《Neurocomputing》

feedback data as our target data or supervised information, and all the other additional information as our auxiliary data. In particular, we focus on how to enable knowledge transfer from some auxiliary data to the target data in order to address the aforementioned sparsity challenge. We discuss some representative transfer learning techniques, aiming to answer the fundamental question of transfer learning [37], i.e., how to transfer . With this focus in our survey, we extend previous categor- ization of transfer learning techniques in collaborative ?ltering [38,43], and answer the above question from two dimensions, including knowledge transfer algorithm styles (i.e., adaptive, collective and inte- grative knowledge transfer) and knowledge transfer strategies (i.e., Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom Neurocomputing http://dx.doi.org/10.1016/j.neucom.2015.11.059 0925-2312/&

2015 Elsevier B.V. All rights reserved. E-mail address: [email protected] Neurocomputing

177 (2016) 447C453 prediction rule, regularization and constraint). Then, we propose a novel and generic knowledge transfer framework and describe some representative works in each category to answer the how to transfer question in detail, in particular the main idea that may be generalized to other applications. Finally, we conclude the survey with some summarized discussions and several exciting future directions. 2. Transfer learning for collaborative recommendation with auxiliary data 2.1. Problem de?nition We have a target data set and an auxiliary data set. In the target data set, we have some feedbacks from n users and m items, which is usually represented as a rating matrix R ? ?rui?n?m and an indi- cator matrix YAf0;

1gn?m , where yui ?

1 means that the feedback rui is observed. In the auxiliary data set, we........