A Generative Framework for Personalized Sticker Retrieval

• URL: http://arxiv.org/abs/2509.17749v4
• Date: Wed, 22 Oct 2025 13:30:28 GMT
• Title: A Generative Framework for Personalized Sticker Retrieval
• Authors: Changjiang Zhou, Ruqing Zhang, Jiafeng Guo, Yu-An Liu, Fan Zhang, Ganyuan Luo, Xueqi Cheng,
• Abstract summary: We propose PEARL, a novel generative framework for personalized sticker retrieval.<n>We make two key contributions: (i) To encode user-specific sticker preferences, we design a representation learning model to learn discriminative user representations, and (ii) To generate stickers aligned with a user's query intent, we propose a novel intent-aware learning objective.<n> Empirical results from both offline evaluations and online tests demonstrate that PEARL significantly outperforms state-of-the-art methods.
• Score: 73.57899194210141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Formulating information retrieval as a variant of generative modeling, specifically using autoregressive models to generate relevant identifiers for a given query, has recently attracted considerable attention. However, its application to personalized sticker retrieval remains largely unexplored and presents unique challenges: existing relevance-based generative retrieval methods typically lack personalization, leading to a mismatch between diverse user expectations and the retrieved results. To address this gap, we propose PEARL, a novel generative framework for personalized sticker retrieval, and make two key contributions: (i) To encode user-specific sticker preferences, we design a representation learning model to learn discriminative user representations. It is trained on three prediction tasks that leverage personal information and click history; and (ii) To generate stickers aligned with a user's query intent, we propose a novel intent-aware learning objective that prioritizes stickers associated with higher-ranked intents. Empirical results from both offline evaluations and online tests demonstrate that PEARL significantly outperforms state-of-the-art methods.

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