The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Amazon Search: The Joy of Ranking Products
Abstract
Amazon is one of the world's largest e-commerce sites and Amazon Search powers the majority of Amazon's sales. As a consequence, even small improvements in relevance ranking both positively influence the shopping experience of millions of customers and significantly impact revenue. In the past, Amazon's product search engine consisted of several hand-tuned ranking functions using a handful of input features. A lot has changed since then. In this talk we are going to cover a number of relevance algorithms used in Amazon Search today. We will describe a general machine learning framework used for ranking within categories, blending separate rankings in All Product Search, NLP techniques used for matching queries and products, and algorithms targeted at unique tasks of specific categories --- books and fashion.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Comparing Click Logs and Editorial Labels for Training Query Rewriting
Zhang, W. V., & Jones, R. (2007) · Cited by 44
Modified Kneser-Ney Smoothing of n-gram Models
James, F. (2000) · Cited by 36
Research works printed but not linked: 1
- 10.5555/891196 · DOI
Cited by
Sorted by citations
Challenges and research opportunities in eCommerce search and recommendations
Tsagkias, M., King, T. H., Kallumadi, S. T., Murdock, V., & de Rijke, M. (2020) · ACM SIGIR Forum · doi:10.1145/3451964.3451966 · Cited by 127
Embedding-based Product Retrieval in Taobao Search
Li, S., Lv, F., Jin, T., Lin, G., Yang, K., Zeng, X., Wu, X.-M., & Ma, Q. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467101 · Cited by 110
Applying Deep Learning to Airbnb Search
Haldar, M., Abdool, M., Ramanathan, P., Xu, T., Yang, S., Duan, H., Zhang, Q., Barrow-Williams, N., Turnbull, B. C., Collins, B. M., et al. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330658 · Cited by 106
Efficient Query Processing for Scalable Web Search
Tonellotto, N., Macdonald, C., & Ounis, I. (2018) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000057 · Cited by 87
Unbiased Measurement of Feature Importance in Tree-Based Methods
Zhou, Z., & Hooker, G. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3429445 · Cited by 87
Metadata
- Authors
- Sorokina, D., & Cantu-Paz, E.
- Affiliations
- A9.com, Palo Alto, CA, USA
- Venue
- Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2016-07-07
- Pages
- 459-460
- OpenAlex
- W2463133191
- Semantic Scholar
- a5c944bff425cbe4bb332be40f0ef55ba2bbb06c
- Cited by
- 71
- References
- 3
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 26 · 2023: 9 · 2022: 10 · 2021: 8 · 2020: 5 · 2019: 7 · 2018: 1
- FWCI
- 2.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- search engine ranking, relevance ranking, e-commerce search engines, product search
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
