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A survey of transfer learning for collaborative recommendation with auxiliary data Weike Pan College of Computer Science and Software Engineering, Shenzhen University, China a r t i c l e i n f o Article history: Received

2 March

2015 Received in revised form

9 November

2015 Accepted

23 November

2015 Communicated by Peng Cui Available online

2 December

2015 Keywords: Collaborative recommendation Auxiliary data Transfer learning a b s t r a c t Intelligent recommendation technology has been playing an increasingly important role in various industry applications such as e-commerce product promotion and Internet advertisement display.

Besides user feedbacks (e.g., numerical ratings) on items as usually exploited by some typical recom- mendation algorithms, there are often some additional data such as users'

social circles and other behaviors. Such auxiliary data are usually related to user preferences on items behind numerical ratings. Collaborative recommendation with auxiliary data (CRAD) aims to leverage such additional information so as to improve personalized services. It has received much attention from both researchers and practi- tioners. Transfer learning (TL) is proposed to extract and transfer knowledge from some auxiliary data in order to assist the learning task on the target data. In this survey, we consider the CRAD problem from a transfer learning view, especially on how to enable knowledge transfer from some auxiliary data, and discuss the representative transfer learning techniques. Firstly, we give a formal de?nition of transfer learning for CRAD (TL-CRAD). Secondly, we extend the existing categorization of TL techniques with three knowledge transfer strategies. Thirdly, we propose a novel and generic knowledge transfer fra- mework for TL-CRAD. Fourthly, we describe some representative works of each speci?c knowledge transfer strategy in detail, which are expected to inspire further works. Finally, we conclude the survey with some summarized discussions and several future directions. &

2015 Elsevier B.V. All rights reserved. 1. Introduction Intelligent recommendation technology [1,4,18,31,45,48] has been a standard component embedded in many Internet systems such as e- commerce and advertisement systems to provide personalized ser- vices. There are two main approaches widely used in personalized recommendation for an active user, i.e., content-based recommenda- tion [3] and collaborative recommendation [14]. Content-based methods promote an item based on the relevance between a candi- date item and the active user'

s consumed items, while collaborative recommendation techniques focus on collective intelligence and exploit the community'

s data so as to recommend preferred items from users with similar tastes. However, both methods are limited to users'

feedbacks of explicit scores or implicit examinations, which may result in a challenging problem, data sparsity, due to the lack of users'

behaviors. Fortunately, there are often some additionally available data besides the users'

feedbacks (e.g., numerical ratings) in a recommen- der system. There are at least four types of auxiliary data as shown in Table 1, such as content information [52,56], time contextual infor- mation [23,36], social or information networks [21,49,54] and addi- tional feedbacks [19,29,39]. These auxiliary data have the potential to help relieve the aforementioned sparsity problem and thus improve the recommendation performance. In this survey, we study on how to exploit different types of auxiliary data in collaborative recommen- dation, which is coined as collaborative recommendation with auxiliary data (CRAD). Speci?cally, we study the CRAD problem from an inductive transfer learning [37] view (instead of unsupervised or transductive transfer learning views [2]), in which we consider the users'