A personalized query auto-completion model with a learned [Reject] token improves both relevance and adaptive toxicity filtering, and is deployed at Kuaishou search.
Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
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abstract
Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions.Today's QAC systems in real-world scenarios face two major challenges:1)intention equivocality(IE): during the user's typing process,the prefix often contains a combination of characters and subwords, which makes the current intention ambiguous and difficult to model.2)intention transfer (IT):previous works make personalized recommendations based on users' historical sequences, but ignore the search intention transfer.However, the current intention extracted from prefix may be contrary to the historical preferences.
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Personalized Query Auto-Completion for Long and Short-Term Interests with Adaptive Detoxification Generation
A personalized query auto-completion model with a learned [Reject] token improves both relevance and adaptive toxicity filtering, and is deployed at Kuaishou search.