A multimodal recommendation system (MRS), which leverages rich multimodal information to model user preferences, has recently attracted significant research interest. Most existing MRSs focus primarily on developing sophisticated encoders for feature extraction, typically relying on simple aggregation of interaction-based features for final predictions. However, this conventional paradigm fails to account for the critical semantic difference between high- and low-rating interactions: while high ratings indicate user preference, low ratings explicitly convey dissatisfaction. Such oversight of negative feedback semantics may significantly limit the system’s recommendation performance. Recently, sign graphs—which model positive and negative feedback signals separately—have gained considerable attention. Inspired by this approach, we propose Sign-Aware Multimodal Graph Recommendation (SiMGR), a novel framework incorporating signed graphs into multimodal recommendation systems. SiMGR fuses multimodal features with signed interactions in a unified graph framework by integrating modality-specific representations and applying user-level thresholds to separate positive and negative subgraphs. A balanced pseudo-edge augmentation strategy is introduced to alleviate sparsity and enhance generalization. Experiments on three public multimodal recommendation datasets show that SiMGR outperforms state-of-the-art baselines, achieving an average 4.28% improvement in NDCG@20.

In today’s information-rich era, users rely heavily on recommender systems to identify relevant content. Graph structures, renowned for their ability to model intricate user-content relationships, have become essential to these systems. However, the accuracy of recommendations hinges critically on the quality of node representations within these graphs. Personalized recommendations strive to enhance uniqueness by maximizing the dissimilarity between representations (known as uniformity) while simultaneously ensuring that the representations align closely with the content users engage with (dubbed as alignment). Nevertheless, balancing these conflicting objectives remains a challenge for optimal recommendation performance. To tackle these challenges, we propose an innovative approach called SIURec, which differs significantly from previous studies. Rather than relying on manual weight selection between uniformity and alignment and optimizing uniformity solely on the final representation, SIURec adopts an adaptive adjustment method that learns the optimal weight between uniformity and alignment automatically. By optimizing uniformity at every convolutional layer, SIURec captures users’ sub-interests more effectively, ultimately leading to improved recommendation accuracy. Experimental results on four datasets demonstrate that SIURec achieves superior learning of uniformity (with an average improvement of 4.26% in accuracy compared to eleven SOTA methods) and exhibits robustness across different hyperparameter settings.

Personalized recommendations are integrated into daily life, but providers may want certain items to become more appealing over time through user interactions, yet this issue is often overlooked. The existing works are often based on the assumption that users will passively accept all intermediate sequences or not explore intention modeling in the targeted nudging process. Both of these factors result in suboptimal performance in the proactive recommendation. In this paper, we propose a novel intention-based targeted multi-round proactive recommendation method, dubbed ITMPRec. We first select target items using a pre-match strategy. Then, we employ a multi-round nudging recommendation method, incorporating a module to quantify users' intention-level evolution, helping choose suitable intermediate items. Additionally, we model users' sensitivity to changes caused by these items. Lastly, we propose an LLM agent as a pluggable component to simulate user feedback, offering an alternative to traditional click models by leveraging the agent's external knowledge and reasoning capabilities. Through extensive experiments on four public datasets, we demonstrate the superiority of ITMPRec compared to eight baseline models.

Abstract In recent years, while algorithm-driven recommendation applications have seen widespread use, their negative impacts have also increasingly raised concerns. To gain a more comprehensive understanding of the impact of different recommendation algorithms, we explored the phenomenon of information cocoons, where users are enveloped by homogenized recommended content, in different algorithm-driven recommender systems. We simulated long-term interactions between users and various algorithm-driven recommender systems, trying to recreate multi-stage recommendation scenarios under the influence of complex factors, and explored whether and to what extent users would fall into information cocoons while analyzing the underlying reasons from the perspective of algorithms. We conducted simulation experiments on two real-world recommendation datasets from different fields. The results show that information cocoons is prevalent across different algorithm-driven recommender systems, and the extent of its occurrence varies. Diversity-oriented recommendations can help alleviate information cocoons but are limited in effectiveness. The ability of diversity-aware re-ranking frameworks to alleviate information cocoons is influenced by the basic recommendation models. Not only considering the diversity of the current recommendation list but also the similarity between items and users’ historical consumption content, we proposed a simple and lightweight re-ranking framework called ICMF. Compared to other re-ranking methods, ICMF avoids an average of 12.48% of users encountering homogenized recommended content.

Today, mainstream recommendation systems have achieved remarkable success in recommending items that align with user interests. However, limited attention has been paid to the perspective of item providers. Content providers often desire that all their offerings, including unpopular or cold items, aredisplayed and appreciated by users. To tackle the challenges ofunfair exhibition and limited item acceptance coverage, we introduce a novel recommendation perspective that enables items to “select” their most relevant users. We further introduce ItemRec, a straightforward plug-and-play approach that leverages mutual scores calculated by any model. The goal is to maximize the recommendation and acceptance of items by users. Through extensive experiments on three real-world datasets, we demonstrate that ItemRec can enhance valid coverage by up to 38.5% while maintaining comparable or superior recommendation quality. This improvement comes with only a minor increase in model inference time, ranging from 1.5% to 5%. Furthermore, when compared to thirteen state-of-the-art recommendation methods across accuracy, fairness, and diversity, ItemRec exhibits significant advantages as well. Specifically, ItemRec achieves an optimal balance between precision and valid coverage, showcasing an efficiency gain ranging from 1.8 to 45 times compared to other fairness-oriented methodologies.

Abstract Deep multi‐model clustering is a challenging task for data analysis since it learns a universal semantic representation to find correct clusters from heterogeneous samples. However, most existing methods 1) lack an effective approach to getting a global representation of visual instances, which results in a huge semantic gap between visual and textual space. 2) hardly consider partial multi‐modal, where each instance is represented by only one modality. In reality, the pairing information for modalities is not available for all instances. To tackle the above issues, we propose a novel model called the Two‐Stage Partial Image‐Text Clustering (TPIT‐C) model. Firstly, we build an interpretable reasoning network to obtain the salient regions and semantic concepts of the scene in order to generate global semantic concepts. Secondly, we construct an adversarial learning module to align textual and visual instances into a unified space by virtue of cycle‐consistency. The experimental evaluations on public unpaired multi‐model datasets illustrated that the proposed method has better performance and the effectiveness of our algorithm in the partial image‐text clustering task.

For many clustering applications, Multi-view data sets are very common. Multi-view clustering aims to exploit information across views instead of individual views, which is promising to improve clustering performance. Note that a high-dimensional data set usually distributes on certain low-dimensional subspace. Thus, many multi-view subspace clustering algorithms have been developed. However, existing multi-view subspace clustering methods rarely perform clustering on the subspace representation of each view simultaneously as well as keep the indicator consistency among the representations, i.e., the same data point in different views should be assigned to the same cluster. In this paper, we propose a novel multi-view subspace clustering method. In our method, we use the indicator matrix to ensure that we perform clustering on the subspace representation of each view simultaneously. And at the same time, a co-regularized term is added to guarantee the consistency of the indicator matrices. Experiments on several real-world multi-view datasets demonstrate the effectiveness and superiority of our proposed method.

Nowadays, using recommendation system to provide users with personalized recommendation service is significantly meaningful. However, traditional collaborative filtering methods may suffer from the cold start problem, while another common recommendation model called content-based recommendation may not have the ability to dig out the potential semantic features of web items sufficiently. In this paper, we propose a novel multi-content clustering collaborative filtering model (MCCCF) for recommendation system. The proposed model can apply multi-view clustering to the mining of the similarity and relevance of web items so that they can be used to improve the classic collaborative filtering. Consequently, the data sparsity problem can be solved. We propose to use multi-view clustering to analyse web items or users from different views such as user ratings and user comments so that it discovers deeper similarity and relevance. At the same time, features from multiple views can be used to complement the user views or item views where the features are deficient, which declines the problem of data sparsity drastically. In this way, we can analyse users' preference by their historical interaction features and supplementary behaviour features to give corresponding recommendation. Above all, the weak spots of the traditional model can be filled in and its performance can be improved. Extensive experiments on real world datasets show that our method outperforms the baselines remarkably.

Multi-view data are prevalent in both machine learning and artificial intelligence. A panoply of multi-view clustering algorithms have been proposed to deal with multiview data. However, most of them just blindly concatenate all the views in spite of characteristic of different views. Self-paced learning is a kind of learning scheme which comes from human learning. It progresses from easy example to complex example during learning process. In analogy with these intuitions, we can learn the easiness of multiple views. Therefore, in this paper, we first present a new Self-Paced Learning Regularizer which is a kind of mixture weighting scheme to allocate different weight to the different view by considering views' complexity. To recap the effectiveness of our self-paced learning regularizer, we propose a novel self-paced learning based multi-view spectral clustering algorithm(SPLMVC), which can define complexity across views and then automatically assign weight to each view. Extensive experiments on real-world multi-view datasets reveal its strength by comparison with other state-of-art methods.