Explainable Product Search with a Dynamic Relation Embedding Model

By Qingyao Ai, Yongfeng Zhang, Keping Bi, W. Bruce Croft. 2019

61 citations recorded by the source corpus.

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. However, they 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, and explanations are generated with logic inferences and entity soft matching on the knowledge graph. Empirical experiments show that our model, which we refer to as the Dynamic Relation Embedding Model (DREM), significantly outperforms the state-of-the-art baselines and has the ability to produce reasonable explanations for search results.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Chen
2. Yongfeng Zhang
3. Hanxiong Chen
4. Shaoyun Shi
5. Xiangnan He
6. Zheng Qin
7. Tat-seng Chua
8. Qingyao Ai

In Information Systems

1. Xu Chen
2. Yongfeng Zhang
3. Hanxiong Chen
4. Shaoyun Shi
5. Xiangnan He
6. Zheng Qin
7. Tat-seng Chua
8. Qingyao Ai

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “By incorporating explicit user queries, the model can be further extended to conduct explainable search over knowledge graphs (Ai et al., 2019).”
• Generate Natural Language Explanations for Recommendation (2021, background): “As a result, researchers have looked into explainable recommendation [46] and search [2] in the recent years [1, 4– 7, 17, 18, 29, 39–41, 46, 47], which can not only provide users with the recommendation lists, but also intutive explanations about why these items are recommended.”