GBPS: A Group-Enhanced Neural Framework for Personalized Search
DOI:
https://doi.org/10.15837/ijccc.2025.4.6698Keywords:
information retrieval, personalized search, grouping method.Abstract
Personalized search aims to improve user experience by tailoring search results to individual preferences. This paper introduces GBPS, a novel group-based personalized search framework that enhances search relevance by dynamically incorporating group user information. Unlike traditional approaches that rely on static, query-independent groupings based on shared clicks, GBPS forms user groups based on representation similarity, capturing deeper semantic connections between users. A hierarchical recurrent neural network (RNN) is employed to model user profiles, and multiple grouping strategies are explored to optimize representation similarities. Experiments on the AOL search log demonstrate that GBPS significantly outperforms some personalized search methods, with improvements of 1.65% in MAP, 1.65% in MRR, and 1.47% in P@1. These results highlight the potential of dynamically leveraging group information to enhance personalized search performance and address the limitations of static grouping methods.
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