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Reference Product Search

Authors:

Yongning WuAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 404 - 410

https://doi.org/10.1145/3308560.3316598

Published: 13 May 2019 Publication History

Abstract

For a product of interest, we propose a search method to surface a set of reference products. The reference products can be used as candidates to support downstream modeling tasks and business applications. The search method consists of product representation learning and fingerprint-type vector searching. The product catalog information is transformed into a high-quality embedding of low dimensions via a novel attention auto-encoder neural network, and the embedding is further coupled with a binary encoding vector for fast retrieval. We conduct extensive experiments to evaluate the proposed method, and compare it with peer services to demonstrate its advantage in terms of search return rate and precision.

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Cited By

Hejazinia MMitsoulis-Ntompos PZhang S(2019)Deep personalized re-targetingProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3345617(1148-1154)Online publication date: 27-Aug-2019
https://dl.acm.org/doi/10.1145/3341161.3345617

Index Terms

Reference Product Search
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Hejazinia MMitsoulis-Ntompos PZhang S(2019)Deep personalized re-targetingProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3345617(1148-1154)Online publication date: 27-Aug-2019
https://dl.acm.org/doi/10.1145/3341161.3345617

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