Cross-Domain Product Search with Knowledge Graph
Rui Zhu, Yiming Zhao, Wei Qu, Zhongyi Liu, Chenliang Li · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
The notion personalization lies on the core of a real-world product search system, whose aim is to understand the user's search intent in a fine-grained level. The existing solutions mainly achieve this purpose through a coarse-grained semantic matching in terms of the query and item's description or the collective click correlations. Besides the issued query, the historical search behaviors of a user would cover lots of her personalized interests, which is a promising avenue to alleviate the semantic gap between users, items and queries. However, as to a specific domain, a user's search behaviors are generally sparse or even unavailable (i.e., cold-start users). How to exploit the search behaviors from the other relevant domain and enable effective fine-grained intent understanding remains largely unexplored for product search. Moreover, the semantic gap could be further aggravated since the properties of an item could evolve over time (e.g., the price adjustment for a mobile phone or the business plan update for a financial item), which is also mainly overlooked by the existing solutions.