Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 5 Mar 2024.
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20 of 56 references.
References · 20
- 2017Attention Is All You Needcited by 59,932
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2018Deep contextualized word representationscited by 2,285
- 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
- 2018Training Tips for the Transformer Modelcited by 85
- 2023Frequency Enhanced Hybrid Attention Network for Sequential Recommendationcited by 52
- 2018Personalized Language Model for Query Auto-Completioncited by 25
- 2023Knowledge Graph Self-Supervised Rationalization for Recommendationcited by 22
- 2023Generative Flow Network for Listwise Recommendationcited by 19
- 2018Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representationscited by 18
- 2018Next Item Recommendation with Self-Attentioncited by 15
8 more
- 2017Relevance-based Word Embeddingcited by 12
- 2018Personalized neural language models for real-world query auto completioncited by 10
- 2020Efficient Neural Query Auto Completioncited by 10
- 2018Generalizing Word Embeddings using Bag of Subwordscited by 9
- 2019Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluationscited by 7
- 2020Character-level Transformer-based Neural Machine Translationcited by 7
- 2023Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendationcited by 7
- 2019A Systematic Study of Leveraging Subword Information for Learning Word Representationscited by 6
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