A Generative Framework for Personalized Sticker Retrieval (2025)

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.

## introduction with the rise of instant messaging applications, online chatting has become an integral part of daily communication. stickers, as expressive visual elements commonly used on platforms such as wechat and whatsapp, play a crucial role in conveying emotions and sentiments. as users increasingly rely on stickers to express themselves, personalized sticker retrieval becomes crucial for retrieving stickers that match users' unique communication styles and emotional preferences @xcite . using generative modeling for sticker retrieval. generative retrieval (gr) is an emerging paradigm * corresponding authors in information retrieval @xcite , where the entire corpus is encoded into model parameters, enabling a single parametric model to directly generate a ranked list of results. typically, a sequence-to-sequence (seq2seq) encoder-decoder architecture is employed to predict the identifiers of documents relevant to a given query. recent studies have demonstrated impressive performance across various retrieval tasks, e.g., passage retrieval and image retrieval @xcite @xcite . however, directly applying existing relevantbased gr methods to personalized sticker retrieval poses unique challenges: (i) different users prefer different stickers. personalized sticker retrieval should incorporate user-specific information, e.g., personal portraits and historical preferences, rather than relying solely on query-sticker semantic associations as in existing gr methods. for instance, given the query "hello", younger users may prefer lively, animated stickers, while older users may favor more restrained or text-based ones. (ii) a single user's preference for sticker properties varies with intent. this calls for intent-aware ranking that aligns with the user's preferences across different sticker properties-be it character ip, visual style, or textual content. for example, for the query "doraemon sleeping", sticker properties related to the doraemon character should be prioritized. in contrast, for "good morning", textual content extracted via ocr may be more important. a personalized sticker retriever. our goal is to develop an effective personalized-learner for gen-erative sticker retrieval (pearl), that can bridge the gap between diverse user expectations and the relevant stickers retrieved by generative modeling. to this end, we need to resolve two key challenges in terms of encoding and decoding. first, how to encode user-specific preferences effectively? in this work, we consider that user-specific preferences are mainly determined by the user's age and gender, as well as historical clickthrough data. in gr, generating document identifiers using dense document representations has been proven effective @xcite . however, user-specific information has not been adequately considered in existing studies. to address the issue, we first categorize users based on their age and gender into distinct user groups, and then for each user group, we design a discriminative representation learning model that captures the unique characteristics of the user group. specifically, three tasks, including user click prediction, user intent prediction and user interest prediction, are involved in the representation learning of the user group using data in the history click log: subsequently, the user group representation is input into the generative model along with the user query for personalized encoding. second, how to decode stickers that align with individual expressive intent? a sticker typically involves multiple properties, such as character ip, ocr textual content, visual style, entity, and meaning. we first generate a product quantization (pq) code for each property of a given sticker as its property identifier @xcite . accordingly, the objective of the gr model is to generate each property identifier of the corresponding stickers for a given input query. we propose an intent-aware loss that reweights the relevance between the input query and different property identifiers based on inferred user intent. to infer user intent, we leverage the chain-of-thought (cot) reasoning capabilities of large language models (llms) @xcite to determine the intent ranking of the query with respect to each property dimension. the intentaware loss is designed to ensure that the property identifiers corresponding to higher-ranked intents receive greater attention. ## problem statement task description. given a textual input query q, the objective of sticker retrieval is to yield a ranked list r of top-k relevant stickers from a large sticker repository @xmath0, s 2 , . . . , s n }, where n denotes the total number of stickers in the repository. as one of the most popular instant messaging platforms, wechat is a representative application scenario of sticker retrieval @xcite . during our investigation of sticker retrieval in wechat, five properties of stickers are considered in this work, including: (i) ocr textual content o refers to the text extracted from the sticker using optical character recognition (ocr) technology. (ii) character ip c refers to intellectual property (ip) related to the characters depicted on the sticker, which could be a well-known character from a movie, tv show, comic book, video game, or any other form of media. (iii) entity e refers to the specific object, symbol, or concept that is primarily depicted in the sticker. (iv) visual style v refers to the specific artistic style that the sticker's design follows. (v) meaning m refers to the intended message, sentiment, or symbolism that the sticker is designed to convey, which is typically provided by the source of the sticker. a more detailed introduction of these properties is provided in appendix d. ## methodology in this section, we present the proposed pearl for personalized sticker retrieval in detail. ## related work sticker retrieval. stickers have gained significant popularity due to their ability to convey emotions, reactions, and nuanced intentions that are difficult to express through plain text @xcite . to retrieve satisfactory stickers for users, liang et al. (2024) proposed a framework dubbed int-ra based on the learning of intention and the cross-modal relationships between conversation context and stickers. zhao et al. (2023) first adapted the clip (radford et al., 2021) model tailored for the domain of emotive stickers. most recently, persrv (chee et al., 2025) first focused on personalized sticker retrieval and introduced user preference modeling by style-based personalized ranking. despite previous efforts, personalized sticker retrieval has not benefited from generative models, which have triggered online system pearl 拜拜 bye 好的 这边就不再打 扰您了 祝您生活愉 快 再见 okay, i won't bother you anymore. have a nice life. bye. 告辞 i'm out 记得想我r emember to miss me古 德拜 去火星咯 goodbye. i'm going to mars. 古德拜 goodbye. 拜拜 bye age: 20-29 gender: female query: 拜拜 (bye-bye) transformative shifts in various areas. generative retrieval. different from the conventional methods that typically formulate information retrieval as a discriminative task @xcite @xcite , generative retrieval (gr) is a new retrieval paradigm in which a single consolidated model is employed to enable the direct generation of relevant docids from queries @xcite . to achieve this, two primary procedures are involved @xcite @xcite , i.e., the indexing process and the retrieval process. the indexing process learns the relationship between documents and the corresponding docids. the retrieval process maps queries to relevant docids. to model personalized user preference in generative retrieval, @xcite proposed an efficient hierarchical encoding-decoding generative retrieval method for large-scale personalized e-commerce search systems. distinct from personalized e-commerce search, which typically involves specific items, the task of personalized sticker retrieval primarily focuses on the abstract expressive intent of stickers and user preference for character ip and sticker style. the fundamental characteristics of stickers highlight that personalized generative retrieval tailored for stickers is a non-trivial challenge worth exploring. ## conclusion in this paper, we focus on personalized sticker retrieval with the promising generative retrieval paradigm. since the sticker retrieval task highly calls for user personalization beyond relance relationships, we propose pearl, a novel generative framework with user-specific information encod-ing and intent-aware sticker decoding. empirical results from both offline evaluations and online experiments indicate the superiority of pearl.