MPR: Multi-Objective Pairwise Ranking

Abstract

The recommendation challenge can be posed as the problem of predicting either item ratings or item rankings. The latter approach has proven more effective. Pairwise learning-to-rank techniques have been relatively successful. Hence, they are popularly used for learning recommender model parameters such as those in collaborative filtering (CF) models. The model parameters are learned by optimizing close smooth approximations of the non-smooth information retrieval (IR) metrics such as Mean Area Under ROC curve (AUC). Targeted campaigns are an alternative to item recommendations for increasing conversion. The user ranking task is referred to as audience retrieval. It is used in targeted campaigns to rank push campaign recipients based on their potential to convert. In this work, we consider the task of efficiently learning a ranking model that provides item recommendations and user rankings simultaneously. We adopt pairwise learning for this task. We refer to our approach as multi-objective pairwise ranking (MPR). We describe our approach and use experiments to evaluate its performance.