Title

Personalized, Sequential, Attentive, Metric-Aware Product Search

Document Type

Article

Publication Title

ACM Transactions on Information Systems

Abstract

The task of personalized product search aims at retrieving a ranked list of products given a user's input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.

DOI

10.1145/3473337

Publication Date

4-1-2022

Keywords

LSTM, metric learning, neural networks, personalized web search, Product search

Comments

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  • OA version (accepted version)
  • No embargo
  • Publisher copyright and source must be acknowledged
  • Must link to publisher version with statement that this is the definitive version and DOI
  • Must state that version on repository is the authors version
  • Set statement to accompany deposit (see policy)

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