SCF: Structured collaborative filtering with heterogeneous implicit feedback

Recommendation systems aim to analyze users’ historical behaviors to recommend items that suit their preferences. In the real world, users’ feedback is usually heterogeneous, such as purchases and examinations, and there are two fundamental challenges, namely the heterogeneity of users’ implicit feedback and the uncertainty of users’ preferences. Previous works have attempted to exploit heterogeneous implicit feedback, but most of them are based on traditional preference assumptions and may not fully learn users’ preferences. To overcome this limitation, we propose a novel solution, i.e., structured collaborative filtering (SCF), which can effectively utilize the complementarity of heterogeneous feedback. Specifically, we address the heterogeneity challenge by designing two types of structured information, i.e., a set of examined items and a group of neighboring users, which integrate examinations into the task of learning users’ purchase preferences. Furthermore, we reduce the uncertainty of users’ preferences by leveraging two preference assumptions defined on randomly constructed item-sets or user-groups. On this basis, we derive four algorithms, including SCF(i,i), SCF(u,i), SCF(i,u), and SCF(u,u), which take advantage of pointwise preference learning to accurately model the latent representations of users and items. Extensive experimentation on three real-world datasets demonstrates that our SCF outperforms the state-of-the-art methods.