Personalized Query Auto-Completion for Long and Short-Term Interests with Adaptive Detoxification Generation
Zhibo Wang, Xiaoze Jiang, Zhiheng Qin, Enyun Yu, Han Li

TL;DR
This paper introduces LaD, a hierarchical personalized query auto-completion model with adaptive detoxification, improving relevance and safety in search suggestions for large-scale industrial deployment.
Contribution
The paper presents a novel hierarchical model capturing long- and short-term user interests with adaptive detoxification via Reject Preference Optimization.
Findings
Significant improvement in online metrics during A/B testing
Deployed at Kuaishou search, influencing hundreds of millions of users
Effective detoxification maintaining relevance in query auto-completion
Abstract
Query auto-completion (QAC) plays a crucial role in modern search systems. However, in real-world applications, there are two pressing challenges that still need to be addressed. First, there is a need for hierarchical personalized representations for users. Previous approaches have typically used users' search behavior as a single, overall representation, which proves inadequate in more nuanced generative scenarios. Additionally, query prefixes are typically short and may contain typos or sensitive information, increasing the likelihood of generating toxic content compared to traditional text generation tasks. Such toxic content can degrade user experience and lead to public relations issues. Therefore, the second critical challenge is detoxifying QAC systems. To address these two limitations, we propose a novel model (LaD) that captures personalized information from both long-term…
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Taxonomy
TopicsDistributed systems and fault tolerance · Cloud Computing and Resource Management · Distributed and Parallel Computing Systems
