Despite the success of Graph Neural Networks (GNNs) in modeling recommender systems as bipartite graphs, their ability to capture diverse user-item relations remains limited by the sparsity of observed interactions, which fails to reveal the underlying latent intents. We propose IACLR (Intention Alignment via Contrastive Learning for bipartite graph Recommendation), a framework that constructs an Intent-Graph by augmenting the bipartite recommendation graphs with an implicit intent layer. Instead of relying solely on observed edges, IACLR introduces a set of intent nodes that bridge users and items through shared semantic and behavioral patterns. These nodes are used to construct an Intent-Graph, where they act as both intermediaries that enrich structural connectivity and global anchors that summarize latent user interests. Within this graph, IACLR performs contrastive alignment across multiple data views and enforces consistency among users, intents, and items, thereby enhancing robustness under data sparsity. Experiments on benchmark datasets (e.g., Amazon-books and Yelp) demonstrate that IACLR consistently outperforms strong graph-based, revealing its effectiveness in capturing fine-grained user–item relationships and integrating multi-faceted signals. The framework is applicable to various recommendation scenarios, including academic paper recommendations, e-commerce, and content platforms.
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Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID) and predicts the next item by autoregressively generating its SID conditioned on the user's history. However, two structural properties of SIDs make ARMs ill-suited. First, intra-item consistency: the n digits jointly specify one item, yet the left-to-right causality trains each digit only under its prefix and blocks bidirectional cross-digit evidence, collapsing supervision to a single causal path. Second, inter-digit heterogeneity: digits differ in semantic granularity and predictability, while the uniform next-token objective assigns equal weight to all digits, overtraining easy digits and undertraining hard digits. To address these two issues, we propose DiffGRM, a diffusion-based GR model that replaces the autoregressive decoder with a masked discrete diffusion model (MDM), thereby enabling bidirectional context and any-order parallel generation of SID digits for recommendation. Specifically, we tailor DiffGRM in three aspects: (1) tokenization with Parallel Semantic Encoding (PSE) to decouple digits and balance per-digit information; (2) training with On-policy Coherent Noising (OCN) that prioritizes uncertain digits via coherent masking to concentrate supervision on high-value signals; and (3) inference with Confidence-guided Parallel Denoising (CPD) that fills higher-confidence digits first and generates diverse Top-K candidates. Experiments show consistent gains over strong generative and discriminative recommendation baselines on multiple datasets, improving NDCG@10 by 6.9%–15.5%. Code is available at: https://github.com/liuzhao09/DiffGRM.
Conversational recommender systems (CRSs) aim to provide real-time recommendations through dynamic interactions between users and the system. Recent studies have revealed the value of personalized information derived from users' historical dialogue records in refining user preferences. However, existing methods often utilize the entire historical dialogue of a user indiscriminately, leading to the issue of cognitive negative transfer, wherein historical dialogue sessions impede rather than facilitate current decision-making. This ultimately degrades the performance of conversational recommendations. To address this issue, inspired by the behavioral decision theory, this paper proposes a novel model, Personalization-Driven Agent Debate for Conversational Recommendation, named EyeCRS. EyeCRS is comprised of two core components: (i) The Multi-Agent Debate Module employs supporting and opposing agents to simulate a human-like debate process, thereby enabling a rigorous evaluation of whether historical dialogue sessions may introduce cognitive negative transfer into the recommender. (ii) The Parametric Injection Module, subsequently, parameterizes the historical dialogues that are validated as positive and injects them into the intrinsic parameter space of the downstream recommender, thereby effectively enhancing the model's ability to internalize and utilize personalized knowledge. Experimental results on two real-world datasets show that EyeCRS effectively mitigates cognitive negative transfer and achieves superior performance in conversational recommendation.
Recently, knowledge graphs have been utilised in recommendation systems to improve accuracy by integrating item-side auxiliary information. However, structural user-side knowledge is difficult to construct and integrate due to inherent scarcity and improper granularity. This paper introduces a graph contrastive learning with Semantic transitions-Enhanced DIffusion architecture based on Large Language Models (LLMs) for user-side knowledge-aware Recommendation (SEDIRec). Specifically, our SEDIRec first leverages LLMs to infer user interests from historical behaviors, integrating this user-side information with item-side and collaborative data to construct main views. Then, two contrastive views are generated using diffusion models with semantic transitions: one at the user-side level and the other at the item-side level. For both contrastive views, we integrate user-side or item-side information with collaborative data to generate a user-item graph. Subsequently, each user-item graph is transformed into collaborative data spaces via diffusion models for generating contrastive views. This procedure not only enhances the alignment between user/item-side information and the semantic spaces of collaborative data but also effectively eliminates noise. Extensive experiments on three datasets reveal the superiority of SEDIRec, especially for users with sparse interactions.
Personalization is well studied in search and recommendation, but personalized question answering remains underexplored due to challenges in inferring preferences from long, noisy, implicit contexts and generating responses that are both accurate and aligned with user expectations. To address this, we propose Pathways of Thoughts (PoT), an inference-stage method that applies to any large language model (LLM) without task-specific fine-tuning. PoT models the thinking as an iterative decision process, where the model dynamically selects among cognitive operations such as reasoning, revision, personalization, and clarification. This enables exploration of multiple reasoning trajectories, producing diverse candidate responses that capture different perspectives. PoT then aggregates and reweights these candidates according to inferred user preferences, yielding a final personalized response that benefits from the complementary strengths of diverse reasoning paths. Experiments on the LaMP-QA benchmark show that PoT consistently outperforms competitive baselines, achieving up to a 10.8% relative improvement. Human evaluation further validates these improvements, with annotators preferring PoT in 66% of cases compared to the best-performing baseline and reporting ties in 15% of cases.
Large Language Models (LLMs) have shown strong potential as conversational agents. Yet, their effectiveness remains limited by deficiencies in robust long-term memory—particularly in complex, long-term Web-based services such as online emotional support. However, existing long-term dialogue benchmarks primarily focus on static and explicit fact retrieval, failing to evaluate agents in these critical scenarios where user information is dispersed, implicit, and continuously evolving. To address this gap, we introduce ES-MemEval, a comprehensive benchmark that systematically evaluates five core memory capabilities—information extraction, temporal reasoning, conflict detection, abstention, and user modeling—in long-term emotional support scenarios, covering question answering, summarization, and dialogue generation tasks. To support the benchmark, we also propose EvoEmo, the first multi-session dataset for personalized long-term emotional support scenarios, capturing fragmented, implicit user disclosures and evolving user states. Extensive experiments on open-source long-context, commercial, and retrieval-augmented (RAG) LLMs reveal that explicit long-term memory is essential to reduce hallucinations and enable effective personalization. At the same time, RAG enhances factual consistency but struggles with temporal dynamics and evolving user states. These findings highlight both the potential and limitations of current paradigms, encouraging the development of more robust memory–retrieval integration in long-term personalized dialogue systems.
Federated Learning (FL) enables knowledge sharing across client models with privacy preservation. A key challenge in FL is to aggregate general knowledge across clients while preserving personalized knowledge for each client. We discover that this challenge primarily stems from the coupling of general and personalized knowledge across different model layers. Existing methods either fail to fully decouple these two types of knowledge or do not leverage them effectively for inference, leading to suboptimal performance. This paper presents Thorki, a new FL system that decouples general and personalized knowledge throughout all model layers. In Thorki, instead of one model, each client stores its personalized knowledge in a small model and general knowledge in a large model. When making an inference, it employs a fusion network to combine the outputs of the two models, leveraging both types of knowledge adaptively. This new model collaboration paradigm allows clients to share their general knowledge in a federated manner without compromising their personalized inference capabilities. Extensive experiments with three models on four datasets demonstrate that Thorki outperforms state-of-the-art FL systems by 0.16%-19.14% in model accuracy and improves time-to-accuracy performance by 2.00x-10.00x.
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.
Implicit feedback is central to modern recommender systems but is inherently noisy, often impairing model training and degrading user experience. At scale, such noise can mislead learning processes, reducing both recommendation accuracy and platform value. Existing denoising strategies typically overlook the entity-specific nature of noise while introducing high computational costs and complex hyperparameter tuning. To address these challenges, we propose EARD (Entity-Aware Reliability-Driven Denoising), a lightweight framework that shifts the focus from interaction-level signals to entity-level reliability. Motivated by the empirical observation that training loss correlates with noise, EARD quantifies user and item reliability via their average training losses as a proxy for reputation, and integrates these entity-level factors with interaction-level confidence. The framework is model-agnostic, computationally efficient, and requires only two intuitive hyperparameters. Extensive experiments across multiple datasets and backbone models demonstrate that EARD yields substantial improvements over state-of-the-art baselines (e.g., up to 27.01% gain in NDCG@50), while incurring negligible additional computational cost. Comprehensive ablation studies and mechanism analyses further confirm EARD's robustness to hyperparameter choices and its practical scalability. These results highlight the importance of entity-aware reliability modeling for denoising implicit feedback and pave the way for more robust recommendation research.
Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed-domain data, leading to negative transfer and diminished model performance. Existing prevailing model-centric paradigm - which relies on complex, customized architectures - struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose Taesar, a data-centric framework for target-aligned sequential regeneration, which employs a contrastive decoding mechanism to adaptively encode cross-domain context into target-domain sequences. It employs contrastive decoding to encode cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures. Experiments show Taesar outperforms model-centric solutions and generalizes to various sequential models. By generating enriched datasets, Taesar effectively combines the strengths of data- and model-centric paradigms. The code accompanying this paper is available at~ https://github.com/USTC-StarTeam/Taesar.
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.
Optimizing recommender systems for objectives beyond accuracy, such as diversity, novelty, and personalization, is crucial for long-term user satisfaction. To this end, the industry has accumulated vast amounts of structured domain knowledge, which we term human priors (e.g., item taxonomies, temporal patterns). This knowledge is typically applied through post-hoc adjustments during ranking or post-ranking. However, this approach remains decoupled from the core model learning, which is particularly undesirable as the industry shifts to end-to-end generative recommendation foundation models. On the other hand, many methods targeting these beyond-accuracy objectives often require architecture-specific modifications and discard these valuable human priors by learning user intent in a fully unsupervised manner. Instead of discarding the human priors accumulated over years of practice, we introduce a backbone-agnostic framework that seamlessly integrates these human priors directly into the end-to-end training of generative recommenders. With lightweight, prior-conditioned adapter heads inspired by efficient LLM decoding strategies, our approach guides the model to disentangle user intent along human-understandable axes (e.g., interaction types, long- vs. short-term interests). We also introduce a hierarchical composition strategy for modeling complex interactions across different prior types. Extensive experiments on three large-scale datasets demonstrate that our method significantly enhances both accuracy and beyond-accuracy objectives. We also show that human priors allow the backbone model to more effectively leverage longer context lengths and larger model sizes.
As users' preferences evolve over time, personalized online services increasingly rely on sequential recommender systems to predict future interactions by modeling patterns in historical user behavior. However, existing methods for sequential recommendation (SR) face two key challenges: they struggle to simultaneously leverage collaborative, semantic, and rating information, and the use of hard labels during training provides limited supervision. In this paper, we introduce SEAR, an LLM-powered Sequential recommEndation framework via fusion of collAborative, semantic, and Rating information. The proposed deep model comprises an embedding layer and a sequence encoder. The embedding layer transforms user-item interactions into three types of embeddings: collaborative, semantic, and rating. The sequence encoder then integrates these embeddings and identifies sequential patterns to model user representations. To enhance the utilization of item semantics, we integrate a large language model (LLM) to extract LLM embeddings. These embeddings are then employed to initialize the semantic embedding layer, collaborative embedding layer, and item embeddings. To capture more nuanced user behavior patterns, we generate preference-weighted soft labels based on the next k interactions. Extensive experiments validate the effectiveness of SEAR, and ablation studies further highlight the distinct contributions of the collaborative, semantic, and rating information.
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.
Despite their success in Collaborative Filtering (CF), Graph Convolutional Networks (GCNs) are often viewed as Low-pass Graph Filters (LGFs) with task-specific supervision, while the influence of the underlying graph's spectral properties on LGF performance remains underexplored. Our analysis reveals that the performance of LGFs is strongly affected by algebraic connectivity. When connectivity is strong, LGFs tend to perform well; when it is weak, their effectiveness diminishes noticeably. This spectral sensitivity highlights an important limitation of existing models. To address this limitation, we propose Graph Booster, a learnable module that adaptively improves graph connectivity by reweighting edges. Unlike heuristic preprocessing, Graph Booster identifies bottleneck edges via spectral embeddings and adjusts their weights with a monotonic network guided by a lightweight graph connectivity regularizer. Integrated into LightGCN framework, our model BoostGCN achieves improvements over state-of-the-art methods, underscoring the significance of algebraic connectivity for graph-based CF.
Recent advances in large language models (LLMs) have enabled more semantic-aware recommendations through natural language generation. Existing LLM for recommendation (LLM4Rec) methods mostly operate in a System 1-like manner, relying on superficial features to match similar items based on click history, rather than reasoning through deeper behavioral logic. This often leads to superficial and erroneous recommendations. Inspired by this, we propose ThinkRec, a thinking-based framework that shifts LLM4Rec from an intuitive system to a rational system. First, ThinkRec introduces a thinking activation mechanism by injecting synthetic reasoning traces, making the recommendation process resemble the Chain of Thought (CoT) reasoning of LLMs. This mechanism analyzes interaction histories, identifies user preferences, and makes decisions based on target items. Furthermore, considering the highly diverse distribution of recommendation data, we propose an instance-wise expert fusion mechanism to reduce the reasoning difficulty. By dynamically assigning weights to expert models based on users' latent features, ThinkRec adapts its reasoning path to individual users, thereby enhancing precision and personalization. Extensive experiments on various real-world web user behavior preference datasets demonstrate that ThinkRec significantly outperforms baselines in terms of recommendation accuracy and interpretability, providing superior recommendations based on a deeper understanding of user intent and a more rigorous reasoning process. Code is available in https://github.com/Yu-Qi-hang/ThinkRec.
Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process of FL incurs considerable computation and communication overhead, which is unfriendly for resource-constrained devices. One-shot FL is a promising approach to addressing communication issues inherent in conventional FL, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups.
The surge in intelligent applications driven by large language models (LLMs) has made it increasingly difficult for bandwidth-limited cloud servers to process extensive LLM workloads in real time without compromising user data privacy. To solve these problems, recent research has focused on constructing cloud-edge consortia that integrate server-based LLM with small language models (SLMs) on mobile edge devices. Furthermore, designing collaborative training mechanisms within such consortia to enhance inference performance has emerged as a promising research direction. However, the cross-domain deployment of SLMs, coupled with structural heterogeneity in SLMs architectures, poses significant challenges to enhancing model performance. To this end, we propose Co-PLMs, a novel co-tuning framework for collaborative training of large and small language models, which integrates the process of structure-agnostic mutual learning to realize knowledge exchange between the heterogeneous language models. This framework employs distilled proxy models (DPMs) as bridges to enable collaborative training between the heterogeneous server-based LLM and on-device SLMs, while preserving the domain-specific insights of each device. The experimental results show that Co-PLMs outperforms state-of-the-art methods, achieving average increases of 5.38% in Rouge-L and 4.88% in EM. Our code has been released at https://github.com/papercode-DFL/Co-PLMs.
Acoustic eavesdropping is a privacy risk, but existing attacks rarely work in real outdoor situations where people make phone calls on the move. We present SuperEar, the first portable system that uses acoustic metamaterials to reliably capture conversations in these scenarios. We show that the threat is real as a practical prototype can be implemented to enhance faint signals, cover the full range of speech with a compact design, and reduce noise and distortion to produce clear audio. We show that SuperEar can be implemented from low-cost 3D-printed parts and off-the-shelf hardware. Experimental results show that SuperEar can recover phone call audio with a success rate of over 80% at distances of up to 4.6 m - more than twice the range of previous approaches. Our findings highlight a new class of privacy threats enabled by metamaterial technology that requires attention.