VIER: Visual Imagination Enhanced Retrieval in Sponsored Search

Yadong Zhang, Yuqing Song, Siyu Lu, Qiang Liu, Xingxing Wang

Abstract: Embedding-based Retrieval (EBR) has been a fundamental component in sponsored-search systems, which retrieves high-quality products for the user's search query by encoding the information of the query, user and product into dense embeddings. However, due to the characteristic of location-based service, the user input queries suffer from two extremes: overly brief queries with vague intentions and lengthy queries with substantial noise, both of which make it challenging to discern the exact user search intent. In fact, the e-consumers typically have a mental imagery of the product they intend to search for, reflecting their specific purchasing intentions. In this paper, we propose a Visual Imagination Enhanced Retrieval model (VIER) to explore the implicit imagery of users. Specifically, we design a visual imagination network to reconstruct the imagery embeddings that capture both coarse-grained query commonalities and fine-grained user personalities. These pseudo-image representations are integrated with the query and user behavior to enhance the understanding of user search intentions for improved retrieval. According to online A/B tests on Meituan sponsored-search system, our method significantly outperforms baselines in terms of revenue, clicks and click-through rate.