A Zero Attention Model for Personalized Product Search

Abstract

Product search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influence on the purchase decision of each customer, it is intuitive that personalization should be beneficial for product search engines. While synthetic experiments from previous studies show that purchase histories are useful for identifying the individual intent of each product search session, the effect of personalization on product search in practice, however, remains mostly unknown. In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine. Results from our preliminary analysis show that the potential of personalization depends on query characteristics, interactions between queries, and user purchase histories. Based on these observations, we propose a Zero Attention Model for product search that automatically determines when and how to personalize a user-query pair via a novel attention mechanism. Empirical results on commercial product search logs show that the proposed model not only significantly outperforms state-of-the-art personalized product retrieval models, but also provides important information on the potential of personalization in each product search session.

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Cited by in corpus (9)

• A Transformer-based Embedding Model for Personalized Product Search
• Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search
• Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
• Learning to Ask: Conversational Product Search via Representation Learning
• Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
• Query-dominant User Interest Network for Large-Scale Search Ranking
• You Are What You Bought: Generating Customer Personas for E-commerce Applications
• Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation
• Bridging Personalization and Control in Scientific Personalized Search