Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism

Yujia Zhou, Qiannan Zhu, Jiajie Jin, Zhicheng Dou

Keywords: Personalized Search, Large Language Models, Memory Mechanism

Abstract: Traditional search engines usually provide identical search results for all users, overlooking individual preferences. To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs. Deep learning-based personalized search methods have shown promise, but they rely heavily on abundant training data, making them susceptible to data sparsity challenges. This paper proposes a Cognitive Personalized Search (CoPS) model, which integrates Large Language Models (LLMs) with a cognitive memory mechanism inspired by human cognition. CoPS employs LLMs to enhance user modeling and user search experience. The cognitive memory mechanism comprises sensory memory for quick sensory responses, working memory for sophisticated cognitive responses, and long-term memory for storing vast historical interactions. CoPS effectively handles new queries using a three-step approach: identifying re-finding behaviors, constructing a user profile with relevant historical information, and ranking documents based on personalized query intent. Experimental results demonstrate the superiority of CoPS over baseline models in zero-shot scenarios.

Track: Search

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

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Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: Yes

Submission Number: 1030