# Search Intenion Network for Personalized Query Auto-Completion in E-Commerce

arXiv: https://arxiv.org/abs/2403.02609 · HTML: https://mlsanity.com/paper/2403.02609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Training Tips for the Transformer Model](https://mlsanity.com/paper/1804.00247/) (2018)
- [Frequency Enhanced Hybrid Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2304.09184/) (2023)
- [Personalized Language Model for Query Auto-Completion](https://mlsanity.com/paper/1804.09661/) (2018)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [Generative Flow Network for Listwise Recommendation](https://mlsanity.com/paper/2306.02239/) (2023)
- [Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations](https://mlsanity.com/paper/1808.09500/) (2018)
- [Next Item Recommendation with Self-Attention](https://mlsanity.com/paper/1808.06414/) (2018)
- [Relevance-based Word Embedding](https://mlsanity.com/paper/1705.03556/) (2017)
- [Personalized neural language models for real-world query auto completion](https://mlsanity.com/paper/1804.06439/) (2018)
- [Efficient Neural Query Auto Completion](https://mlsanity.com/paper/2008.02879/) (2020)
- [Generalizing Word Embeddings using Bag of Subwords](https://mlsanity.com/paper/1809.04259/) (2018)
- [Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations](https://mlsanity.com/paper/1905.01997/) (2019)
- [Character-level Transformer-based Neural Machine Translation](https://mlsanity.com/paper/2005.11239/) (2020)
- [Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2311.13188/) (2023)
- [A Systematic Study of Leveraging Subword Information for Learning Word Representations](https://mlsanity.com/paper/1904.07994/) (2019)

## Built on by (most cited first, 1 of 1)

- [Personalized Query Auto-Completion for Long and Short-Term Interests with Adaptive Detoxification Generation](https://mlsanity.com/paper/2505.20966/) (2025)

## 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.