Search-based recommendation is one of the most critical application scenarios in e-commerce platforms. Users' complex search contexts—such as spatiotemporal factors, historical interactions, and current query's information—constitute an essential part of their decision-making, reflecting implicit preferences that complement explicit query terms. Modeling such rich contextual signals and their intricate associations with candidate items remains a key challenge. Although numerous efforts have been devoted to building more effective search methods, existing approaches still show limitations in integrating contextual information, which hinders their ability to fully capture user intent. To address these challenges, we propose a context-aware reasoning-enhanced generative search framework for better understanding the complicated context. Specifically, the framework first unifies heterogeneous user and item contexts into textual representations or text-based semantic identifiers and aligns them. To overcome the lack of explicit reasoning trajectories, we introduce a self-evolving post-training paradigm that iteratively combines supervised fine-tuning and reinforcement learning to progressively enhance the model's reasoning capability. In addition, we identify potential biases in existing RL algorithms when applied to search scenarios and present a debiased variant of GRPO to improve ranking performance. Extensive experiments on search log data collected from a real-world e-commerce platform demonstrate that our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.
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The vulnerability of graph-based recommender systems to spurious correlations has become a significant obstacle to their practical deployment, hindering their robustness in out-of-distribution (OOD) scenarios. While existing approaches offer partial solutions, they are limited by fundamental shortcomings: model-centric approaches reliant on predefined causal graphs often suffer from suboptimal performance due to complex and dynamic environmental influences. These methods typically require identifying an environmental label or performing feature decoupling, but hidden environments are often difficult to model. Furthermore, existing general feature decoupling methods fail to account for the unique structural characteristics of graphs. To overcome these challenges, we advocate for a shift towards explicit, geometrically-grounded disentanglement. Hyperbolic geometry is particularly suited for this task due to its capacity to model the inherent hierarchies of user interests. We introduce C-HyPOD : Causal Hyperbolic Representation Learning with Prototype Orthogonal Disentanglement, a novel framework designed for graph-based OOD recommendation. Unlike traditional methods, C-HyPOD transforms disentanglement into a concrete geometric task. It introduces a global interest space by learning a single set of universal interest prototypes. They provides a superior geometric foundation for ensuring these prototypes are well-separated and semantically distinct. To ensure a complete separation and prevent information leakage, a targeted orthogonality constraint is then applied. This constraint purifies the aggregated causal representation by forcing it to be orthogonal to the spurious representation in the tangent space, thereby eliminating their linear correlation. Extensive experiments on four public datasets demonstrate that C-HyPOD significantly improves OOD robustness and recommendation performance, surpassing state-of-the-art methods.
Scaling laws have enabled large language models(LLMs) to achieve remarkable performance and strong generalization across diverse language understanding tasks, including few-shot, in-context, and zero-shot learning. While prior studies in large-scale collaborative filtering(CF) have revealed clear relationships between model performance and scaling factors such as data size and model capacity, little attention has been given to how heterogeneous datasets can be synergistically combined for recommender systems(RS). In particular, it remains unclear whether systematically integrating diverse recommendation datasets can yield scaling behaviors analogous to those observed in LLMs, while simultaneously addressing challenges such as cold-start recommendation and cross-domain transfer. In this paper, we present RecCLIP, a multimodal framework that reformulates user--item interactions as visual representations compatible with vision--language models(VLMs). RecCLIP compresses interaction signals and employs prompt-based ranking to enable unified representation across heterogeneous data sources. Extensive experiments reveal consistent power-law scaling trends with respect to data size, and demonstrate that RecCLIP achieves superior performance in both cold-start and cross-domain transfer scenarios. Our findings underscore the importance of data-centric design in recommender systems and provide practical insights into scaling them effectively.The code for replication is available at https://github.com/jinliwei-1/RecCLIP.
Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in traditional single-behavior approaches. However, existing MBR methods face fundamental challenges: they lack principled frameworks to model complex confounding effects from user behavioral habits and item multi-behavior distributions, struggle with effective aggregation of heterogeneous auxiliary behaviors, and fail to align behavioral representations across semantic gaps while accounting for bias distortions. To address these limitations, we propose MCLMR, a novel model-agnostic causal learning framework that can be seamlessly integrated into various MBR architectures. MCLMR first constructs a causal graph to model confounding effects and performs interventions for unbiased preference estimation. Under this causal framework, it employs an Adaptive Aggregation module based on Mixture-of-Experts to dynamically fuse auxiliary behavior information and a Bias-aware Contrastive Learning module to align cross-behavior representations in a bias-aware manner. Extensive experiments on three real-world datasets demonstrate that MCLMR achieves significant performance improvements across various baseline models, validating its effectiveness and generality. All data and code will be made publicly available. For anonymous review, our code is available at the following the link: https://github.com/gitrxh/MCLMR.
Rapid development of web services has led to an explosion of multimodal content, making multimodal recommender systems (MRSs) vital tools for mitigating information overload. Current MRSs have achieved remarkable progress by incorporating advanced technologies such as Graph Neural Networks (GNNs) and Large Language Models (LLMs). However, these studies still suffer from the semantic shift problem. Generally, item's multimodal content usually contain multiple objects, including target object (core content of item) and auxiliary objects (decorations of item). Existing MRSs overlooked this distinction, failing to prevent auxiliary objects from dominating the representation, leading to biased item representation. To address this issue, we propose a model-agnostic framework ''TargetMR''. Concretely, TargetMR comprises two core modules, including Object Disentangler and Object Identifier. The Object Disentangler decouples item text and image into multiple objects via text syntactic parsing and image segmentation. The Object Identifier performs knowledge distillation based on LLMs to efficiently identify the target text object. It then identifies the target image object through cross-modal semantic evaluation. Moreover, this module refines the representation of image target object by optimizing the semantic correlation. Owing to the model-agnostic design of TargetMR, it can be integrated into various backbone MRSs. Extensive experiments on three benchmark datasets show that TargetMR consistently improves the performance of five backbone MRSs, with an average improvement of 12.26%. Our codes are available at https://github.com/gutang-97/TargetMR/.
Multi-criteria recommender systems (MCRSs) are becoming increasingly important in the Web ecosystem, where platforms such as e-commerce sites and review portals allow users to evaluate items from multiple perspectives. By leveraging criterion-level ratings rather than relying solely on overall scores, MCRSs can enhance personalization and more accurately capture user preferences. However, existing multi-criteria recommendation methods often fail to explicitly model user-specific preferences across criteria or to incorporate item–criterion signals into representation learning. To solve these problems, we propose TaTriGR, a Targeting-Aware Tripartite Graph Recommender, which jointly models user–item–criterion interactions in a unified tripartite graph and integrates user-specific criterion weights through targeting mechanisms. Specifically, TaTriGR encodes user–criterion targeting through personalized weights, while incorporating item–criterion performance as an additional channel to enrich item semantics. A lightweight propagation mechanism then diffuses information across the tripartite structure, and a formal score decomposition shows that predictions satisfy fundamental multi-criteria decision making (MCDM) properties. To further reinforce targeting, TaTriGR introduces two auxiliary objectives: Ideal Point Distillation and Lexicographic Consistency, which encourage criterion-consistent user representations and rankings. Extensive experiments on three real-world datasets demonstrate the effectiveness of TaTriGR, showing relative gains up to 16.1% over the best baseline models. Our implementations are available at https://github.com/nunu1995/TaTriGR.
Session-based recommendation (SBR), which focuses on next-item prediction for anonymous users based on short-term interaction sequences, has garnered increasing attention from researchers. While graph neural networks (GNNs) have become predominant in modeling complex item transition patterns, our empirical study reveals two critical limitations in existing GNN-based SBR methods. On the one hand, they struggle to differentiate between sessions sharing the same last item, resulting in indistinguishable session representations. On the other hand, the inherent popularity bias in session data leads to the over-recommendation of popular items. Inspired by contrastive learning techniques, this paper presents a unified mitigation framework for Same lAst-item confusion in Graph lEarning (SAGE) for SBR. In SAGE, we first obtain normalized session embeddings on constructed session graphs. We then build positive and negative samples of sessions through dual forward propagations and a novel negative sample selection strategy, followed by calculating contrastive loss. Finally, the enhanced session embeddings are utilized for prediction. Extensive experiments on two real-world datasets demonstrate that integrating SAGE with various state-of-the-art GNN-based SBR methods significantly improves their original performances.
Can we design effective recommender systems free from users, IDs, and GNNs? Recommender systems are central to personalized content delivery across domains, with top-K item recommendation being a fundamental task to retrieve the most relevant items from historical interactions. Existing methods rely on entrenched design conventions, often adopted without reconsideration, such as storing per-user embeddings (user-dependent), initializing features from raw IDs (ID-dependent), and employing graph neural networks (GNN-dependent). These dependencies incur several limitations, including high memory costs, cold-start and over-smoothing issues, and poor generalization to unseen interactions. In this work, we propose AlphaFree, a novel recommendation method free from users, IDs, and GNNs. Our main ideas are to infer preferences on-the-fly without user embeddings (user-free), replace raw IDs with language representations (LRs) from pre-trained language models (ID-free), and capture collaborative signals through augmentation with similar items and contrastive learning, without GNNs (GNN-free). Extensive experiments on various real-world datasets show that AlphaFree consistently outperforms its competitors, achieving up to around 40% improvements over non-LR-based methods and up to 5.7% improvements over LR-based methods, while significantly reducing GPU memory usage by up to 69% under high-dimensional LRs.
Federated cross-domain recommendation (Federated CDR) aims to collaboratively learn personalized recommendation models across heterogeneous domains while preserving data privacy. Recently, large language model (LLM)-based recommendation models have demonstrated impressive performance by leveraging LLMs' strong reasoning capabilities and broad knowledge. However, adopting LLM-based recommendation models in Federated CDR scenarios introduces new challenges. First, there exists a risk of overfitting with domain-specific local adapters. The magnitudes of locally optimized parameter updates often vary across domains, causing biased aggregation and overfitting toward domain-specific distributions. Second, unlike traditional recommendation models (e.g., collaborative filtering, bipartite graph-based methods) that learn explicit and comparable user/item representations, LLMs encode knowledge implicitly through autoregressive text generation training. This poses additional challenges for effectively measuring the cross-domain similarities under heterogeneity. To address these challenges, we propose an LLM-based framework for federated cross-domain recommendation, FeDecider. Specifically, FeDecider tackles the challenge of scale-specific noise by disentangling each client's low-rank updates and sharing only their directional components. To handle the need for flexible and effective integration, each client further learns personalized weights that achieve the data-aware integration of updates from other domains. Extensive experiments across diverse datasets validate the effectiveness of our proposed FeDecider.
Live streaming platforms have recently gained popularity due to their immediacy and entertainment value, highlighting the need for streaming recommender systems that can adapt to the dynamic nature of evolving content, real-time interactions, and changing user interests. The ''live room'' plays a central role in modeling this dynamic environment, as it not only connects users with streamers but also serves as a key channel for collecting fine-grained user feedback. More specifically, the frequent interactions of users within a live room provide detailed dynamic collaboration information, reflecting the streamer's real-time topic and users' dynamic interests. However, existing studies have not thoroughly investigated the dynamics of room-level collaborative information. In this paper, we address this gap by emphasizing two perspectives: the evolving tripartite interaction information among rooms, streamers, and users, and the real-time intra-room collaboration information. We propose DCGLive, a Dynamic Collaboration-aware Graph learning approach for Live streaming recommendation. Specifically, we first construct two dynamic bipartite graphs to perceive the evolving tripartite interaction and generate real-time representations of streamers, rooms, and users. To account for the dynamic nature of live streaming, we design a set of non-parametric, collaboration-aware indicators that weight intra-room interactions based on both temporal recency and frequency, while guiding the embedding updating process for both users and rooms. Additionally, to address the cold-start challenge of newly created live rooms in real time, we propose a room representation initialization mechanism that balances both the relevance and the dynamics among different rooms hosted by the same streamer. Experiments conducted on both commercial and public datasets demonstrate that DCGLive consistently outperforms the baseline models. Our code is available at https://github.com/imgkkk574/DCGLive.
Graph-based collaborative filtering has advanced by modeling higher-order interactions, yet performance remains constrained by underlying geometric assumptions and propagation schemes. User-item interaction graphs typically exhibit pronounced topological heterogeneity, whereas existing methods rely on a fixed, homogeneous geometry and employ tangent space aggregation. To address these fundamental limitations, this paper introduces Adaptive Geometric Collaborative Filtering (AGCF), a novel method rooted in Hamiltonian dynamics, which reframes representation learning as a physical process evolving on a time-varying manifold. AGCF is distinguished by an integrated design comprising: (1) a learnable, node-dependent Riemannian metric that construct a continuous heterogeneous manifold aligned with local topology; (2) unified dynamic trajectories that achieve intrinsic propagation without tangent space approximations; (3) a channel-wise metric that captures semantic anisotropy in the feature space. We rigorously prove global existence and uniqueness of the induced dynamics and explain the mechanism enabling long-range information propagation. Extensive experiments on five benchmark datasets show consistent gains over representative baselines.
Graph-based social recommendation (SocialRec) has emerged as a powerful extension of graph collaborative filtering (GCF), which leverages graph neural networks (GNNs) to capture multi-hop collaborative signals from user-item interactions. These methods enrich user representations by incorporating social network information into GCF, thereby integrating additional collaborative signals from social relations. However, existing GCF and graph-based SocialRec approaches face significant challenges: they incur high computational costs and suffer from limited scalability due to the large number of parameters required to assign explicit embeddings to all users and items. In this work, we propose PULSE (Parameter-efficient User representation Learning with Social Knowledge), a framework that addresses this limitation by constructing user representations from socially meaningful signals without creating an explicit learnable embedding for each user. PULSE reduces the parameter size by up to 50% compared to the most lightweight GCF baseline. Beyond parameter efficiency, our method achieves state-of-the-art performance, outperforming 13 GCF and graph-based social recommendation baselines across varying levels of interaction sparsity, from cold-start to highly active users, through a time- and memory-efficient modeling process.
Sequential recommender systems play a vital role in alleviating the challenge of information overload. Although contrastive learning has been increasingly adopted in sequential recommendation to enhance model performance, most existing approaches rely on predefined data augmentation strategies—such as random noise injection or neuron dropout—to generate contrasting views. These strategies, however, often overlook the inherent semantic similarity between the original sequence and its augmented views, which can inadvertently distort user intent and compromise recommendation accuracy. To address this issue, we propose an Adaptive Contrastive Learning framework for Sequential Recommendation (ACLSRec), which incorporates learnable perturbation and restoration networks for adaptive augmentation. The framework dynamically perturbs and restores user representations, thereby ensuring semantic consistency across augmented views and effectively capturing evolving user interest patterns through contrastive learning. Extensive experiments on real-world datasets demonstrate that ACLSRec achieves superior recommendation accuracy compared to several competitive baselines. This work not only establishes a new baseline for sequential recommendation but also paves the way for developing more robust and adaptive contrastive learning frameworks in recommender systems. The source code is available at https://github.com/xiaomizhou778/ACLSRec.
By allowing each client to refer to the knowledge from other clients while retaining their specific characteristics, partial knowledge sharing has become one of the main approaches to realizing personalized federated learning (pFL). Representative techniques of partial knowledge sharing propose sharing the feature extractor while customizing the classifier head of the neural network. Although such methods achieve great success, the underlying principle behind them remains yet to be comprehensively understood. A fundamental problem is whether it is really appropriate to fully share the feature extractor. Based on the theory of neural collapse, in this paper, we demonstrate both theoretically and empirically that the feature extractor should be partially shared rather than fully shared. More specifically, we identify a substantial inconsistency between the fused global feature representations and expected local feature representations, and thus it is necessary to preserve partially customized layers of the feature extractor for enhancing personalized representations. Based on this discovery, we further propose a novel method called pFedDKS which detaches the shared global knowledge and customized local knowledge by providing detached feature prototypes. Extensive experiments on various datasets and models show that pFedDKS outperforms state-of-the-arts.
Multimodal recommendation advocates integrating the multimodal features of items with historical user behaviors to enhance recommendation accuracy across various online media platforms. The majority of existing methods concentrate on leveraging cross-modal learning over multimodal features to augment node representations. However, these approaches are confronted with two key challenges: i) augmented representations offer limited information gain for interactive prediction in the collaborative view, and ii) semantic discrepancy between the collaborative view and modality-augmented features remains inadequately addressed. To overcome these obstacles, we present a new Multi-view Semantic Contrastive Alignment (MSCA) approach for multimodal recommendation, which models and aligns node representations from multiple views. Specifically, we introduce a multi-view semantic pattern encoder that learns basic embeddings from the collaborative view and independently captures augmented semantic patterns from the item-item structural view and intra-modal view. Furthermore, a semantic contrastive alignment task is designed to mitigate the semantic divergence between collaborative embeddings and augmented representations by maximizing the mutual consistency between them, thereby facilitating an effective integration of both. Comprehensive experiments on three benchmark datasets confirm that the proposed MSCA consistently excels over diverse state-of-the-art baselines.
Lifelong sequential recommendation aims to model users' long-term interests by leveraging their entire interaction history, but the high computational overhead caused by ultra-long sequences poses a core challenge. To address this, existing methods generally adopt the subsequence learning strategies to shorten the input sequence, which can be divided into two categories: (1) Sequence compression methods compress long sequences into multiple subsequence representations through strategies such as uniform segmentation and clustering; (2) Top-k retrieval methods filter the subsequence related to the target item from long sequences via target attention mechanisms or retrieval mechanisms and learn its representation. However, these two types of methods still face challenges in subsequence representation learning: (1) Sequence compression methods struggle to simultaneously balance the high similarity of items within subsequences and smooth temporal continuity (i.e., small temporal intervals between adjacent items), resulting in incorrect learning of subsequence representations; (2) Top-k retrieval methods lose a large amount of effective context information when the length of the retrieved subsequence is much smaller than the original sequence, resulting in incomplete sequence representations. To overcome these challenges, we propose a novel lifelong sequential recommendation method with adaptive subsequence compression and contextual fusion. Specifically, an adaptive subsequence compression module is first designed: it utilizes gradient policy sampling to achieve adaptive segmentation of subsequences, thereby retaining their temporal continuity, and introduces a reward function to enhance the similarity of items within subsequences. Second, a subsequence context fusion module is constructed: it leverages causal cross-attention to fuse recent interactions with their subsequence context and to capture correlations among recent items, thereby learning more complete and accurate sequence representations. We conduct extensive experiments on three public long-sequence recommendation datasets. Experimental results demonstrate that our proposed method consistently outperforms a variety of strong baselines in both predictive accuracy and computational efficiency.
Sequential recommendation (SR) learns user preferences based on their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most users can only interact with a handful of items, while the majority of items are seldom consumed. This pervasive long-tail challenge limits the model's ability to learn user preferences. Despite previous efforts to enrich tail items/users with knowledge from head parts or improve tail learning through additional contextual information, they still face the following issues: 1) They struggle to improve the situation where interactions of tail users/items are scarce, leading to incomplete preferences learning for the tail parts. 2) Existing methods often degrade overall or head parts performance when improving accuracy for tail users/items, thereby harming the user experience. In this work, we propose Tail-Aware Data Augmentation (TADA) for long-tail sequential recommendation, which enhances the interaction frequency for tail items/users while maintaining head performance, thereby promoting the model's learning capabilities for the tail. Specifically, we first capture the co-occurrence and correlation among low-popularity items by a linear model. Building upon this, we design two tail-aware augmentation operators, T-Substitute and T-Insert. The former replaces the head item with a relevant item, while the latter utilizes co-occurrence relationships to extend the original sequence by incorporating both head and tail items. The augmented and original sequences are mixed at the representation level to preserve preference knowledge. We further extend the mix operation across different tail-user sequences and augmented sequences to generate richer augmented samples, thereby improving tail performance. Comprehensive experiments demonstrate the superiority of our method. The codes are provided at https://github.com/KingGugu/TADA.
Large Language Models (LLMs) with powerful reasoning capabilities, offer new opportunities for recommendation systems (RS) , especially in understanding user preference. However, the direct application of LLMs to this task faces two major challenges: (1) limited context windows struggle to process the extensive user behavior sequences in real-world scenarios; (2) inherent hallucination effect can lead LLMs to infer spurious preferences that contradict true user intent, thereby degrading recommendation quality. To address this, we propose Rec 2, a Reinforcement learning-constrained segmented user modeling framework for recommendation. The framework segments lengthy behavior sequences into multiple segments and generates user preferences in a cascaded fashion. To suppress hallucinations and align with user intent, we introduce reinforcement learning, which uses the subsequent behavior segment as a supervisory signal to constrain and reward the preference inference process on the current segment, thereby generating more predictive and high-fidelity user preferences. Finally, the aligned LLMs infer preferences for each behavior segment; these are then processed by a specially-designed dynamic preference learning module to model preference evolution and are ultimately aggregated into a unified, dynamic long-term user preference embedding. This representation can be integrated into any recommendation model to boost its performance. Extensive experiments demonstrate that Rec2 significantly enhances recommendation performance by effectively capturing dynamic and authentic user preferences.
Recommendation systems aim to learn user interests from historical behaviors and deliver relevant items. Recent methods leverage large language models (LLMs) to construct and integrate semantic representations of users and items for capturing user interests. However, user behavior theories suggest that truly understanding user interests requires not only semantic integration but also semantic reasoning from explicit individual interests to implicit group interests. To this end, we propose an Iterative Semantic Reasoning Framework (ISRF) for generative recommendation. ISRF leverages LLMs to bridge explicit individual interests and implicit group interests in three steps. First, we perform multi-step bidirectional reasoning over item attributes to infer semantic item features and build a semantic interaction graph capturing users' explicit interests. Second, we generate semantic user features based on the semantic item features and construct a similarity-based user graph to infer the implicit interests of similar user groups. Third, we adopt an iterative batch optimization strategy, where individual explicit interests directly guide the refinement of group implicit interests, while group implicit interests indirectly enhance individual modeling. This iterative process ensures consistent and progressive interest reasoning, enabling more accurate and comprehensive user interest learning. Extensive experiments on the Sports, Beauty, and Toys datasets demonstrate that ISRF outperforms state-of-the-art baselines. The code is available at https://github.com/htired/ISRF.
Quantum computing is an emerging research area. This paper investigates why and how quantum computing can be integrated into recommender systems. Although some existing recommendation methods explore quantum concepts, they either remain theoretical without empirical validation or provide limited insight into the use of quantum computing for designing core functions in recommendation. To fill these gaps, we first analyze the potential advantages of quantum computing for two key components (i.e., representation learning and matching learning) in recommender algorithms and formulate corresponding hypotheses. Then, based on our analysis and the quantum computing operations, we propose three quantum-enhanced recommendation paradigms. To show the extensibility of our paradigms, we further apply them to the graph-based and social recommendation scenarios. We conduct extensive experiments on the six real-world datasets, comparing our methods with various baselines. Experimental results not only validate our hypotheses but also show the strong performance of our proposed methods.