Traditional sequential recommendation (SR) models learn low-dimensional item ID embeddings from user-item interactions, often overlooking textual information such as item titles or descriptions. Recent advances in Large Language Models (LLMs) have inspired a surge of research that encodes item textual information with high-dimensional semantic embeddings, and designs transformation methods to inject such embeddings into SR models. These embedding transformation strategies can be categorized into two types, both of which exhibits notable drawbacks: 1) adapter-based methods suffer from pronounced dimension collapse, concentrating information into a few dominant dimensions; 2) SVD-based methods are rigid and manual, considering only a few principal spectral components while discarding rich information in the remaining spectrum.

The emergence of generative models enables the creation of texts and images tailored to users' preferences. Existing personalized generative models have two critical limitations: lacking a dedicated paradigm for accurate preference modeling, and generating unimodal content despite real-world multimodal-driven user interactions. Therefore, we propose personalized multimodal generation, which captures modal-specific preferences via a dedicated preference model from multimodal interactions, and then feeds them into downstream generators for personalized multimodal content. However, this task presents two challenges: (1) Gap between continuous preferences from dedicated modeling and discrete token inputs intrinsic to generator architectures; (2) Potential inconsistency between generated images and texts. To tackle these, we present a two-stage framework called Discrete Preference learning for Personalized Multimodal Generation (DPPMG). In the first stage, to accurately learn discrete modal-specific preferences, we introduce a modal-specific graph neural network (a dedicated preference model) to learn users' modal-specific preferences, which preferences are then quantized into discrete preference tokens. In the second stage, the discrete modal-specific preference tokens are injected into downstream text and image generators. To further enhance cross-modal consistency while preserving personalization, we design a cross-modal consistent and personalized reward to fine-tune token-associated parameters. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model in generating personalized and consistent multimodal content.

Click-through rate (CTR) prediction is a fundamental task in modern recommender systems. In recent years, the integration of large language models (LLMs) has been shown to effectively enhance the performance of traditional CTR methods. However, existing LLM-enhanced methods often require extensive processing of detailed textual descriptions for large-scale instances or user/item entities, leading to substantial computational overhead. To address this challenge, this work introduces LLaCTR, a novel and lightweight LLM-enhanced CTR method that employs a field-level enhancement paradigm. Specifically, LLaCTR first utilizes LLMs to distill crucial and lightweight semantic knowledge from small-scale feature fields through self-supervised field-feature fine-tuning. Subsequently, it leverages this field-level semantic knowledge to enhance both feature representation and feature interactions. In our experiments, we integrate LLaCTR with six representative CTR models across four datasets, demonstrating its superior performance in terms of both effectiveness and efficiency compared to existing LLM-enhanced methods. Our code is available at https://github.com/istarryn/LLaCTR.

The rapid advancement of multimodal large language models has enabled agents to operate mobile devices by directly interacting with graphical user interfaces, opening new possibilities for mobile automation. However, real-world mobile tasks are often complex and allow for multiple valid solutions. This contradicts current mobile agent evaluation standards: offline static benchmarks can only validate a single predefined ''golden path'', while online dynamic testing is constrained by the complexity and non-reproducibility of real devices, making both approaches inadequate for comprehensively assessing agent capabilities. To bridge the gap between offline and online evaluation and enhance testing stability, this paper introduces a novel graph-structured benchmarking framework. By modeling the finite states observed during real-device interactions, it achieves static simulation of dynamic behaviors. Building on this, we develop ColorBench, a benchmark focused on complex long-horizon tasks. It supports evaluation of multiple valid solutions, subtask completion rate statistics, and atomic-level capability analysis. ColorBench contains 175 tasks (74 single-app, 101 cross-app) with an average length of over 13 steps. Each task includes at least two correct paths and several typical error paths, enabling quasi-dynamic interaction.

Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy risks. However, existing methods overlook a critical issue, i.e., the stable learning of a generalized item embedding throughout the federated recommender system training process. Item embedding plays a central role in facilitating knowledge sharing across clients. Yet, under the cross-device setting, local data distributions exhibit significant heterogeneity and sparsity, exacerbating the difficulty of learning generalized embeddings. These factors make the stable learning of generalized item embeddings both indispensable for effective federated recommendation and inherently difficult to achieve. To fill this gap, we propose a new federated recommendation framework, named Federated Recommendation with Generalized Embedding Learning (FedRecGEL). We reformulate the federated recommendation problem from an item-centered perspective and cast it as a multi-task learning problem, aiming to learn generalized embeddings throughout the training procedure. Based on theoretical analysis, we employ sharpness-aware minimization to address the generalization problem, thereby stabilizing the training process and enhancing recommendation performance. Extensive experiments on four datasets demonstrate the effectiveness of FedRecGEL in significantly improving federated recommendation performance. Our code is available at https://github.com/anonymifish/FedRecGEL.

Sequential recommenders generate recommendations based on users’ historical interaction sequences. However, in practice, these sequences are often contaminated by noisy interactions, which can arise from various factors such as clickbait, the influence of prominently positioned items, or accidental interactions. Such noise can significantly degrade recommendation performance. Accurately identifying such noisy interactions without additional information is particularly challenging due to the absence of explicit supervisory signals indicating noise. Large Language Models (LLMs), equipped with extensive open knowledge and semantic reasoning abilities, offer a promising avenue to bridge this information gap. However, employing LLMs for denoising in sequential recommendation presents notable challenges: (1) Direct application of pretrained LLMs may not be competent for the denoising task, frequently generating nonsensical responses; (2) Fine-tuning on the denoising task can partially mitigate the issue of generating nonsensical outputs. However, even after fine-tuning, the reliability of LLM outputs remains questionable, especially given the complexity of the denoising task and the inherent hallucination issue of LLMs. To tackle these challenges, we propose LLM4DSR, a tailored approach for denoising sequential recommendation using LLMs. We constructed a self-supervised fine-tuning task to activate LLMs’ capabilities to identify noisy items and suggest replacements. Furthermore, we developed an uncertainty estimation module that ensures only high-confidence responses are utilized for sequence corrections. Remarkably, LLM4DSR is model-agnostic, allowing corrected sequences to be flexibly applied across various recommendation models. To the best of our knowledge, this is the first work that employs LLMs for sequential recommendation denoising while addressing the unique challenges of adapting LLMs to this task. Extensive experiments conducted on three real-world datasets across two noise settings validate the effectiveness of LLM4DSR, demonstrating an average improvement of 12.9% in NDCG@20. The code is available at https://github.com/WANGBohaO-jpg/LLM4DSR .

Personalized recommender systems are critical for enhancing user engagement across a range of digital platforms. However, conventional approaches rely heavily on centralized data collection, raising significant privacy concerns. Federated recommender systems (PFRS) address these concerns by decentralizing model training, ensuring user data privacy. Despite the progress, existing methods still struggle with capturing the multi-faceted nature of user and transferring global knowledge effectively. In this work, we propose FedMUR, a novel federated recommendation framework that models user representation as a Gaussian mixture distribution, capturing users' multi-faceted characteristics. Each Gaussian component corresponds to a distinct interest facet, with adaptive mixture weights representing the user's preference intensity toward each facet. To facilitate knowledge transfer, FedMUR constructs global consistent prototypes that encode shared behavioral trends across users via popularity-weighted optimal transport. These prototypes enhance local models by injecting global shared patterns into personalized representation learning. Extensive experiments across several real-world datasets demonstrate that FedMUR significantly outperforms existing state-of-the-art federated recommendation systems.

Industrial recommender systems usually train models incrementally to grasp recent interests of users. However, a fundamental issue of these incremental updated models is their tendency to overfit current data while neglecting past information. Specifically, we have observed that the data distribution of real systems exhibits periodic drifts, leading to periodic fluctuations of prediction bias. To alleviate the above bias fluctuations while minimizing the loss of recent interests, we propose TPIA, a Training-free approach for Periodic Interest Augmentation in incremental recommendation. Specifically, after the latest model is trained, we first calculate the importance score of each model in the previous period. Then, we merge these models based on the importance scores. To minimize information loss due to interference of parameters during model merging, we further develop a method for trimming redundant and abnormal parameters. Offline experiments on both public and private datasets demonstrate the effectiveness of TPIA. It has also been deployed on a large-scale industrial recommender system, and has shown a notable 1.61% increase in CVR and a 1.97% increase in CPM, along with enhanced stability in prediction bias.

Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS).Given the fundamental gap between the mechanism of LLMs and the requirement of RS, researchers have focused on fine-tuning LLMs with recommendationspecific data to enhance their performance.Language Modeling Loss (LML), originally designed for language generation tasks, is commonly adopted.However, we identify two critical limitations of LML: 1) it exhibits significant divergence from the recommendation objective; 2) it erroneously treats all fictitious item descriptions as negative samples, introducing misleading training signals.To address these limitations, we propose a novel Masked Softmax Loss (MSL) tailored for fine-tuning LLMs on recommendation.MSL improves LML by identifying and masking invalid tokens that could lead to fictitious item descriptions during loss computation.This strategy can effectively avoid the interference from erroneous

Federated Recommendation (FedRec) enables joint training across a large number of clients without centralizing user interaction data. However, existing FedRec methods overlook two key challenges, i.e. (1) sufficiently explore the global item embedding space, and (2) effectively achieve local and global collaboration. The former is caused by client sparsity, which leads to suboptimal item embeddings and subsequently impacts the global item embedding in both the dimension and sample views. The latter arises from the lack of modeling the relative importance of local and global contributions to personalized user preferences. To address the above challenges, we propose FedIAR which contains two modules, i.e., item embedding dual-view exploration and adaptive local-global fusion. The first module enhances the global item embedding by reducing redundancy in the dimension view and capturing latent item relationships in the sample view, improving representational capacity. The second module enables the adaptive fusion of local and global item embeddings based on the user preference representation, achieving personalized optimum for recommendation. Extensive experiments on six datasets demonstrate the effectiveness of FedIAR in improving federated recommendation performance.

Customer lifetime value (LTV) is crucial to companies who are intending to adopt personalized promoting strategies to optimize the profits. However, LTV prediction in the scenario of online App advertising usually suffers from label sparsity issue, towards which existing methods designed complex model structures but ignored the information contained in intermediate user behaviors. Moreover, previous works mainly focus on fitting the overall LTV distribution, overlooking the fact that LTV in online App advertising consists of sources with diverse data distributions and thus resulting in sub-optimal solutions. In this paper, we propose a novel Progressive Tasks guided Multi-Source Network (PTMSN) to tackle the aforementioned problems. Specifically, a Cascaded Sub-task Module (CSM) is introduced to alleviate data sparsity by modeling reliance between explicit interactions and implicit monetization. In addition, as the overall LTV is assembled from multiple sources, we propose a divide-and-conquer scheme named Multi-source Integrating Module (MIM) to disentangle the original single target into several source distributions and model in a fine-grained manner. Extensive offline experiments on real-world industrial datasets compared to state-of-the-art baseline models validate the effectiveness of our approach. PTMSN has been successfully deployed in industrial online advertising system, serving various business scenarios and acquiring 2.97% absolute ROI gains.

Federated graph learning involves training graph neural networks distributively on local graphs and aggregating model parameters in a central server. However, existing methods fail to effectively capture and leverage the inherent global structures, hindering local structural modeling. To address this, we propose Federated Graph Factorization (FedGF), which enhances structural knowledge via privacy-preserving graph factorization. Specifically, FedGF includes three modules, i.e., global structure reconstruction (GSR), local structure exploration (LSE), and global-local structure alignment (GLSA). Firstly, GSR factorizes client graphs into a series of learnable graph atoms and conducts reconstruction to capture the globally shared structure. Then, LSE explores the local structure, mining potential but unrevealed connections within client subgraphs. GLSA further aligns the global and local structure to alternatively refine the graph atoms and GNN model, enhancing the overall structural modeling. Extensive experiments on six datasets consistently validate the effectiveness of \modelname.

Cross-Domain Recommendation (CDR) has been widely investi- gated for solving long-standing data sparsity problem via knowl- edge sharing across domains. In this paper, we focus on the Multi- Modal Cross-Domain Recommendation (MMCDR) problem where different items have multi-modal information while few users are overlapped across domains. MMCDR is particularly challenging in two aspects: fully exploiting diverse multi-modal information within each domain and leveraging useful knowledge transfer across domains. However, previous methods fail to cluster items with similar characteristics while filtering out inherit noises within different modalities, hurdling the model performance. What is worse, conventional CDR models primarily rely on overlapped users for domain adaptation, making them ill-equipped to handle scenarios where the majority of users are non-overlapped. To fill this gap, we propose Joint Similarity Item Exploration and Overlapped User Guidance (SIEOUG) for solving the MMCDR problem. SIEOUG first proposes similarity item exploration module, which not only obtains pair-wise and group-wise item-item graph knowledge, but also reduces irrelevant noise for multi-modal modeling. Then SIEOUG proposes user-item collaborative filtering module to aggregate user/item embeddings with the attention mechanism for collaborative filtering. Finally SIEOUG proposes overlapped user guidance module with optimal user matching for knowledge sharing across domains. Our empirical study on Amazon dataset with several different tasks demonstrates that SIEOUG significantly outperforms the state-of-the-art models under the MMCDR setting.

With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as unlearning target. However, attackers can extract private information from the model even if it has not been explicitly encountered during training. We name this unseen information as attribute and treat it as unlearning target. To protect the sensitive attribute of users, Attribute Unlearning (AU) aims to make target attributes indistinguishable. In this article, we focus on a strict but practical setting of AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be performed after the training of the recommendation model is completed. To address the PoT-AU problem in recommender systems, we propose a two-component loss function. The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers. We further extend this measurement to handle multi-class attribute cases with efficient computational overhead. The second component is regularization loss, where we explore a function-space measurement that effectively maintains recommendation performance compared to parameter-space regularization. We use stochastic gradient descent algorithm to optimize our proposed loss. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed methods.

Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendation systems (RS). Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. Specifically, We first simulate the industrial RS environment that maintains respective models in multiple domains, each of them is trained in the incremental mode. Then, for improving the effectiveness, we design two extractors to fully extract domain-invariant information from the latest source domain models at the domain level and the representation level respectively. Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference. Experiments conducted on one production dataset and two public datasets verify the effectiveness and efficiency of DIIT.

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the high inference latency of LLMs significantly restricts their practical deployment. To address this issue, this work investigates knowledge distillation from cumbersome LLM-based recommendation models to lightweight conventional sequential models. It encounters three challenges: 1) the teacher’s knowledge may not always be reliable; 2) the capacity gap between the teacher and student makes it difficult for the student to assimilate the teacher’s knowledge; 3) divergence in semantic space poses a challenge to distill the knowledge from embeddings.