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.

In recent years, there has been a burgeoning interest in multimodal recommender systems within the recommendation systems domain. These systems aim to understand user preferences by leveraging both user interaction data and multimodal information associated with items. This approach frequently results in superior recommendation accuracy compared to traditional models that rely solely on user-item interactions. Despite the advancements of these methods, there is a relatively low utilization of image features in propagating item-item characteristics, an overreliance on text feature similarity, and a frequent neglect of the deep relationships between items, users, and modalities. In response to these challenges, we introduce a novel model termed LLMs-Enhanced Hyper-Knowledge Graph Recommender for Multimodal Recommendation (DOGE). DOGE utilizes large language models (LLMs) to understand image information under the guidance of text information, generating cross-modal features that effectively enhance the relationship between text and image modalities. Subsequently, DOGE constructs a Hyper-Knowledge Graph (HKG) using user-item interaction information and modality features enhanced by large language models. This graph encompasses a wide range of item-item and user-user binary relations and hyper-relations, effectively expanding the feature propagation mechanisms and mitigating the overreliance on text modality. By learning on heterogeneous user-item graphs and homogeneous item-item, user-user graphs, DOGE enhances potential effective propagation between item features and user features, acquiring more effective feature representations of users and items. Comprehensive experimentation across three public real-world datasets illustrates that DOGE attains state-of-the-art (SOTA) performance, exhibiting a 7.2% improvement over the strongest baseline.

Multi-criteria decision making (MCDM) and preference learning (PL) are crucial subfields of intelligent decision-making, both aiming to aid decision-makers (DMs) in selecting, classifying, or ranking alternatives. While MCDM and PL can complement each other to some extent, existing approaches combining MCDM and PL often struggle with large data volumes and complex relational information. To address this, we propose a novel approach called ID-GMLM that integrates graph models and large language models (LLMs) for intelligent decision-making. It reformulates decision-making as a high-parallelism ranking function in the graph domain, using graph neural networks (GNNs) to learn and understand complex relationships between alternatives or criteria, and LLMs to parse and quantify the preferences of DMs. ID-GMLM features a multi-task learning framework that optimizes the primary task of predicting alternative rankings while modeling criterion interactions through the auxiliary task. Additionally, ID-GMLM incorporates a parameter tuning network based on criterion weights and an attention network, allowing the model to adaptively adjust to the context of the current task and the evolving preferences of DMs. Experiments on benchmark datasets demonstrate that ID-GMLM achieves significant performance improvements, inheriting the interpretability and intuitive appeal of MCDM while leveraging the computational efficiency and high accuracy of PL.

The application of Large Language Models (LLMs) is rapidly expanding in fields such as natural language processing and computer vision. However, due to the enormous number of model parameters, while their emergent capabilities enhance performance, they also incur significant computational and storage costs, particularly during inference. This makes it particularly challenging to deploy large models in resource-constrained environments. To address this issue, we propose an innovative pruning method based on local entropy, aimed at improving model performance while reducing computational costs. Our approach leverages a small amount of calibration data and calculates the importance of weights based on the local entropy of weights and input activations, applying it to both unstructured and semi-structured pruning without the need for complex weight updates or retraining. Experimental results demonstrate that our method shows superior performance across models of various scales. Specifically, at a sparsity rate of 50%, our method significantly reduces model perplexity without the need for weight updates and outperforms existing pruning techniques such as SparseGPT and Wanda in zero-shot tasks. The code is available at: this https URL.

Multimodal recommender systems enhance recommendation performance by integrating information from different modalities (e.g., text and images). A common approach is to link items with high modality similarity in modality graphs, helping users explore their interests more broadly. However, existing methods often introduce noise when enhancing modality graphs, making it challenging to effectively balance performance and accuracy. To address this issue, we propose an Interest Tree Augmented Modality Graph RecommendER for Multimodal Recommendation (TAMER). In this framework, we first redistribute item modality features using various component analysis methods to ensure more reliable item similarity within modality graphs. Next, we construct interest graphs based on reliable semantic relationships and prune the interest graphs into multiple interest trees. These interest trees are then applied to the multimodal item-item homogeneous graph to extend potential links within the modality homogeneous graph. The interest tree-based enhancement method effectively captures high-order relationships in the modality graph while avoiding noisy links. The effectiveness of the proposed method is demonstrated through comprehensive experiments on three real-world datasets. Compared with the strongest baseline methods, our method achieves an average improvement of 9.98% across four evaluation metrics. The source code is available at https://github.com/Z-last-ONE/TAMER.

In the era of digital personalization, multi-criteria recommender systems (MCRSs) play a vital role in capturing the multi-dimensional nature of user preferences by considering multiple evaluation criteria rather than relying on a single overall rating. However, existing approaches to MCRSs face challenges in managing graph sparsity, criterion independence, and leveraging semantic information for recommendation tasks. To address these limitations, we propose a novel framework named Large L anguage Models-augmented Light G raph Model for M ulti- c riteria Rec ommendation ( LGMcRec ). LGMcRec integrates the strengths of graph neural networks (GNNs) and large language models (LLMs) to improve the representation and recommendation capabilities of MCRSs. In our model, we construct a tripartite graph structure that captures user-item interactions, item-criterion associations, and criterion interdependencies, effectively addressing issues of sparsity and unmodeled correlations in multi-criteria data. We extend the LightGCN architecture to learn embeddings over this graph, which are further enriched through semantic alignment with embeddings generated by LLMs from textual user and item profiles. To bridge the gap between graph-based and LLM-based embeddings, we employ a contrastive learning approach that maximizes the mutual information between the two embedding spaces, ensuring cohesive and comprehensive user and item representations. Experimental results on three MCRS datasets demonstrate that LGMcRec achieves significant performance improvements over state-of-the-art methods.