A Dynamic Product-aware Learning Model for E-commerce Query Intent Understanding

Query intent understanding is a fundamental and essential task in searching, which promotes personalized retrieval results and users' satisfaction. In E-commerce, query understanding is particularly referring to bridging the gap between query representations and product representations. In this paper, we aim to map the queries into the predefined tens of thousands of fine-grained categories extracted from the product descriptions. The problem is very challenging in several aspects. First, a query may be related to multiple categories and to identify all the best matching categories could eventually drive the search engine for high recall and diversity. Second, the same query may have dynamic intents under various scenarios and there is a need to distinguish the differences to promote accurate categories of products. Third, the tail queries are particularly difficult for understanding due to noise and lack of customer feedback information. To better understand the queries, we firstly conduct analysis on the search queries and behaviors in the E-commerce domain and identified the uniqueness of our problem (e.g. longer sessions). Then we propose a Dynamic Product-aware Hierarchical Attention (DPHA) framework to capture the explicit and implied meanings of a query given its context information in the session. Specifically, DPHA automatically learns the bidirectional query-level and self-attentional session-level representations which can capture both complex long range dependencies and structural information. Extensive experimental results on a real E-commerce query data set demonstrate the effectiveness of the proposed DPHA compared to the state-of-art baselines.