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 recommendation 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 practitioners. 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 definition 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 framework for TL-CRAD. Fourthly, we describe some representative works of each specific 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.