Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
This work aims to enhance search engine efficiency for e-commerce users, but appears incremental as it builds on existing personalized QAC approaches.
The paper tackled the problem of personalized query auto-completion in e-commerce by addressing intention equivocality and intention transfer, proposing a method to improve recommendation accuracy.
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.