Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems

Data sparsity is a well-recognized issue for Top-N item recommendation, which depends on user preference gathered from their historical behaviors (i.e., implicit feedback). However, only few works have considered multiple types of auxiliary implicit feedback (e.g, click, wanted) when building recommendation models. This paper aims to resolve the data sparsity problem by (a) generating target data (e.g., purchase) from a linear regression of auxiliary feedback, and from the nearest neighbors with a set of purchased items in multiple dimensions; (b) proposing a novel ranking model to accommodate both the original and generated data. We provide an intuitive comprehension regarding the relationship between one kind of auxiliary feedback and target feedback. A series of experiments are conducted on two real-world datasets and demonstrate the superiority of our approach to other counterparts.