Learning a hierarchical embedding model for personalized product search

Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, W. Bruce Croft

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

Abstract

Product search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not con.ned to retrieving relevant products. Instead, it focuses on .nding items that satisfy the needs of individuals and lead to a user purchase.The unique characteristics of product search make search personalization essential for both customers and e-shopping companies. Purchase behavior is highly personal in online shopping and users o.en provide rich feedback about their decisions (e.g. product reviews). However, the severe mismatch found in the language of queries, products and users make traditional retrieval models based on bag-of-words assumptions less suitable for personalization in product search. In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from different levels with their associated language data. Our contributions are three-fold: (1) our work is one of the initial studies on personalized product search; (2) our hierarchical embedding model is the .rst latent space model that jointly learns distributed representations for queries, products and users with a deep neural network; (3) each component of our network is designed as a generative model so that the whole structure is explainable and extendable. Following the methodology of previous studies, we constructed personalized product search benchmarks with Amazon product data. Experiments show that our hierarchical embedding model significantly outperforms existing product search baselines on multiple benchmark datasets.

Original languageEnglish (US)
Title of host publicationSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages645-654
Number of pages10
ISBN (Electronic)9781450350228
DOIs
StatePublished - Aug 7 2017
Externally publishedYes
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: Aug 7 2017Aug 11 2017

Publication series

NameSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Country/TerritoryJapan
CityTokyo, Shinjuku
Period8/7/178/11/17

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • Latent Space Model
  • Personalization
  • Product Search
  • Representation Learning

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