# Generic Intent Representation in Web Search

arXiv: https://arxiv.org/abs/1907.10710 · HTML: https://mlsanity.com/paper/1907.10710/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Natural Language Understanding with Privacy-Preserving BERT](https://mlsanity.com/paper/2104.07504/) (2021)
- [Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2205.03763/) (2022)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies](https://mlsanity.com/paper/2309.13063/) (2023)
- [TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling](https://mlsanity.com/paper/2109.04562/) (2021)
- [IntentVizor: Towards Generic Query Guided Interactive Video Summarization](https://mlsanity.com/paper/2109.14834/) (2021)
- [IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce](https://mlsanity.com/paper/2406.10173/) (2024)
- [Noise-Robust Dense Retrieval via Contrastive Alignment Post Training](https://mlsanity.com/paper/2304.03401/) (2023)
- [TempoFormer: A Transformer for Temporally-aware Representations in Change Detection](https://mlsanity.com/paper/2408.15689/) (2024)
- [Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems](https://mlsanity.com/paper/2405.12327/) (2024)
- [APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization](https://mlsanity.com/paper/2104.11384/) (2021)
- [DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search](https://mlsanity.com/paper/2104.11760/) (2021)

## Abstract (arXiv)

This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search. Leveraging large scale user clicks from Bing search logs as weak supervision of user intent, GEN Encoder learns to map queries with shared clicks into similar embeddings end-to-end and then finetunes on multiple paraphrase tasks. Experimental results on an intrinsic evaluation task - query intent similarity modeling - demonstrate GEN Encoder's robust and significant advantages over previous representation methods. Ablation studies reveal the crucial role of learning from implicit user feedback in representing user intent and the contributions of multi-task learning in representation generality. We also demonstrate that GEN Encoder alleviates the sparsity of tail search traffic and cuts down half of the unseen queries by using an efficient approximate nearest neighbor search to effectively identify previous queries with the same search intent. Finally, we demonstrate distances between GEN encodings reflect certain information seeking behaviors in search sessions.