Inferring Networks of Substitutable and Complementary Products
- 10 August 2015
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing 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. In economics, 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. Such relationships are essential as they help us to identify items that are relevant to a user's search. Our goal in this paper is to learn the semantics of substitutes and complements from the text of online reviews. We treat this as a supervised learning problem, trained using networks of products derived from browsing and co-purchasing logs. Methodologically, we build topic models that are trained to automatically discover topics from product reviews 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.This publication has 26 references indexed in Scilit:
- On the design of LDA models for aspect-based opinion miningPublished by Association for Computing Machinery (ACM) ,2012
- Supercharging recommender systems using taxonomies for learning user purchase behaviorProceedings of the VLDB Endowment, 2012
- Collaborative topic modeling for recommending scientific articlesPublished by Association for Computing Machinery (ACM) ,2011
- Block-LDA: Jointly modeling entity-annotated text and entity-entity linksPublished by Society for Industrial & Applied Mathematics (SIAM) ,2011
- Advances in Collaborative FilteringPublished by Springer Science and Business Media LLC ,2010
- On centroidal voronoi tessellation—energy smoothness and fast computationACM Transactions on Graphics, 2009
- Extracting product features and opinions from reviewsPublished by Association for Computational Linguistics (ACL) ,2005
- Pulse: Mining Customer Opinions from Free TextLecture Notes in Computer Science, 2005
- Amazon.com recommendations: item-to-item collaborative filteringIEEE Internet Computing, 2003
- Updating quasi-Newton matrices with limited storageMathematics of Computation, 1980