Method, system, and non-transitory machine-readable information storage medium for personalized substitute product recommendation

Abstract

Product recommendation is a very important aspect of e-commerce applications. Traditional product recommendation systems recommend products similar to a query image provided by a user and allows minimum or no personalization. It is challenging to incorporate personalization due to the presence of overlapping fine-grained attributes, variations in attribute style and visual appearance, small inter-class variation and class imbalance in the images of products. Embodiments of present disclosure address these challenges by a method of personalized substitute product recommendation using Personalized Attribute Search Networks (PAtSNets) comprising neural network layers interleaved with Attentive Style Embedding (ASE) modules to generate attribute-aware feature representation vector of a query image provided by the user and conforming to the personalization instructions specified by the user. This feature representation vector is then used to recommend substitute products to the user. Thus, embodiments of present disclosure enable accurate substitute product recommendation suiting user requirements.