# Personalizing Search Results Using Hierarchical RNN with Query-aware Attention

arXiv: https://arxiv.org/abs/1908.07600 · HTML: https://mlsanity.com/paper/1908.07600/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [LLMs + Persona-Plug = Personalized LLMs](https://mlsanity.com/paper/2409.11901/) (2024)
- [USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence](https://mlsanity.com/paper/2109.15012/) (2021)
- [From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking](https://mlsanity.com/paper/2208.10226/) (2022)
- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- [TPRM: A Topic-based Personalized Ranking Model for Web Search](https://mlsanity.com/paper/2108.06014/) (2021)
- [Bridging Personalization and Control in Scientific Personalized Search](https://mlsanity.com/paper/2411.02790/) (2024)

## Abstract (arXiv)

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.