# PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling

arXiv: https://arxiv.org/abs/2111.12614 · HTML: https://mlsanity.com/paper/2111.12614/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2021.

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Trustworthiness in Retrieval-Augmented Generation Systems: A Survey](https://mlsanity.com/paper/2409.10102/) (2024)
- [Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation](https://mlsanity.com/paper/2402.07092/) (2024)
- [FIT-RAG: Black-Box RAG with Factual Information and Token Reduction](https://mlsanity.com/paper/2403.14374/) (2024)
- [From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking](https://mlsanity.com/paper/2208.10226/) (2022)
- [MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling](https://mlsanity.com/paper/2210.08753/) (2022)
- [Bridging Personalization and Control in Scientific Personalized Search](https://mlsanity.com/paper/2411.02790/) (2024)
- [Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap](https://mlsanity.com/paper/2501.14826/) (2025)

## Abstract (arXiv)

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made great progress in extracting personal signals from the query log and learning user representations. However, neural personalized search is extremely dependent on sufficient data to train the user model. Data sparsity is an inevitable challenge for existing methods to learn high-quality user representations. Moreover, the overemphasis on final ranking quality leads to rough data representations and impairs the generalizability of the model. To tackle these issues, we propose a Personalized Search framework with Self-supervised Learning (PSSL) to enhance data representations. Specifically, we adopt a contrastive sampling method to extract paired self-supervised information from sequences of user behaviors in query logs. Four auxiliary tasks are designed to pre-train the sentence encoder and the sequence encoder used in the ranking model. They are optimized by contrastive loss which aims to close the distance between similar user sequences, queries, and documents. Experimental results on two datasets demonstrate that our proposed model PSSL achieves state-of-the-art performance compared with existing baselines.