Interaction-Rich Transfer Learning for Collaborative Filtering with Heterogeneous User Feedback

A real recommender system can usually make use of more than one type of user feedback--for example, numerical ratings and binary ratings-to learn a user´s true preferences. Recent work has proposed a transfer learning algorithm called transfer by collective factorization (TCF) to exploit such heterogeneous user feedback. TCF performs via sharing data-independent knowledge and modeling data-dependent effects simultaneously. However, TCF is a batch algorithm and updates the model parameters only once after scanning all data, which might not be efficient enough for real systems. This article proposes a novel and efficient transfer learning algorithm called interaction-rich transfer by collective factorization (iTCF), which extends the efficient collective matrix factorization (CMF) algorithm by providing more interactions between the user-specific latent features. The assumption under iTCF is that the predictability with regards to the same user´s rating behaviors in two related data is likely to be similar. Considering the shared predictability, the authors derive novel update rules for iTCF in a stochastic algorithmic framework. The advantages of iTCF include its efficiency compared with TCF, and its higher prediction accuracy compared with CMF. Experimental results on three real-world datasets show the effectiveness of iTCF over the state-of-the-art methods.