Inferring Networks of Substitutable and Complementary Products

TL;DR

This paper introduces a supervised learning approach using topic models to infer networks of substitutable and complementary products from product reviews and features, enhancing recommendation systems.

Contribution

It presents a novel method that combines topic modeling and link prediction to identify product relationships from large-scale review data.

Findings

Effective inference of product networks from reviews

High accuracy in predicting substitute and complement links

Scalable approach tested on Amazon dataset

Abstract

In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.