Explainable Product Search With A Dynamic Relation Embedding Model
2019 · Qingyao Ai, Yongfeng Zhang, Keping Bi, et al.
Abstract
Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. They, however, ignore the problem that there is a gap between how systems and customers perceive the relevance of items. Without explanations, users may not understand why product search engines retrieve certain items for them, which consequentially leads to imperfect user experience and suboptimal system performance in practice. In this work, we tackle this problem by constructing explainable retrieval models for product search. Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session. Ranking is conducted based on the relationship between users and items in the latent space, a
Authors
(none)
Tags
Stats
Related papers
- Neural IR Meets Graph Embedding: A Ranking Model For Product Search (2019)11.85
- Embedding-based Product Retrieval In Taobao Search (2021)13.70
- Large Reasoning Embedding Models: Towards Next-generation Dense Retrieval Paradigm (2025)0.00
- Unified Embedding Based Personalized Retrieval In Etsy Search (2023)2.26
- Session Context Embedding For Intent Understanding In Product Search (2024)0.00
- Multimodal Semantic Retrieval For Product Search (2025)3.58
- Embracing Structure In Data For Billion-scale Semantic Product Search (2021)0.00
- Llms As Sparse Retrievers:a Framework For First-stage Product Search (2025)0.00