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.

翻译：将信息检索构建为生成式建模的一种变体，特别是利用自回归模型为给定查询生成相关标识符，近来已引起广泛关注。然而，其在个性化贴纸检索中的应用仍鲜有探索，并面临独特挑战：现有的基于相关性的生成式检索方法通常缺乏个性化，导致多样化的用户期望与检索结果之间存在不匹配。为填补这一空白，我们提出了PEARL——一种新颖的个性化贴纸检索生成式框架，并做出两项关键贡献：（i）为编码用户特定的贴纸偏好，我们设计了一种表征学习模型以学习具有区分性的用户表征。该模型通过利用个人信息与点击历史的三个预测任务进行训练；（ii）为生成与用户查询意图相符的贴纸，我们提出了一种新颖的意图感知学习目标，该目标优先考虑与更高排序意图相关联的贴纸。离线评估与在线测试的实证结果表明，PEARL显著优于现有最先进方法。