Boosting recommendation in unexplored categories by user price preference

Abstract

State-of-the-art methods for product recommendation encounter a significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category and attempting to be diversified. In this article, we investigate the challenging problem of product recommendation in unexplored categories and discover that the price, a factor comparable across categories, can improve the recommendation performance significantly. We introduce the price utility concept to characterize users' sense of price and propose three different utility functions. We show that user price preference in a category is a distribution and we mine typical user price preference patterns based on three different types of distance between distributions. We fuse user price preference through regularization and joint factorization to boost recommendation performance in both browsing and buying shopping orientations. Experimental results show that fusing user price preference improves performance in a series of recommendation tasks: unexplored category recommendation, product recommendation under a given unexplored category, and product recommendation under generic unexplored categories.