Inferring Networks of Substitutable and Complementary Products
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.
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For each query, the dataset provides a list of up to 40 results together with their ESCI relevance judgements (Exact, Substitute, Complement, or Irrelevant).
“For each query, the dataset provides a list of up to 40 results, together with their ESCI relevance judgements (Exact, Substitute, Complement, or Irrelevant) indicating the relevance of the product to the query [11].”
From Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search (2022) · cites this paper as [11]
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- Authors
- McAuley, J., Pandey, R., & Leskovec, J.
- Affiliations
- University of California San Diego (US)
- Pinterest (United States) (US)
- Stanford University (US)
- Venue
- Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2015-08-10
- Pages
- 785-794
- OpenAlex
- W1991418309
- Cited by
- 645
- References
- 0
- Citations by year
- 2026: 30 · 2025: 39 · 2024: 35 · 2023: 50 · 2022: 50 · 2021: 56 · 2020: 64 · 2019: 80 · 2018: 85 · 2017: 99 · 2016: 53 · 2015: 4
- FWCI
- 69.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Keywords
- product substitution, product complementarity, recommender systems, product networks, network inference, mobile devices
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