Transfer to Rank for Heterogeneous One-Class Collaborative Filtering

摘要

Heterogeneous one-class collaborative filtering is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this article, we propose to model purchases and browses from a new perspective, i.e., users' roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed role-based transfer to rank (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e., mixer) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on three large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.