To address the interaction sparsity, recent recommendation techniques introduce multimodal semantics to enrich collaborative signals for modeling users' interests. However, these models neglect to depict interaction behaviors and omit to involve diverse behavioral patterns, which are the core characters of the recommendation scenario. Therefore, we propose an intent-augmented multimodal graph embedding (IMGE) model for multimodal recommendation, which constructs an interaction graph with multimodal interactions and augments the graph with behavioral interactions to promote recommendation. Unlike the conventional interaction graph, edges are encoded with multimodal semantics, and intent auxiliary nodes are injected into the neighbors of user/item nodes. IMGE innovatively modifies graph convolution by explicitly involving edge signals, which aggregates collaborative signals from the intent-augmented multimodal graph to embed both semantic and behavioral collaborative signals. Experiments on three public datasets demonstrate the superiority of the proposed IMGE, especially on the Baby dataset achieving 40.78% improvement by NDCG, verifying the effectiveness of encoding interactions with semantic and behavioral signals for recommendation.

Video captioning aims to generate natural language descriptions of video content. Recent methods extract temporal and spatial information separately and use dataset-specific prior knowledge to enhance caption quality. However, they may be inadequate in joint spatiotemporal modeling and lack the utilization of commonsense knowledge, making it difficult to fully understand the video. To address these issues, this paper proposes a dual-stream state-space model (DSSM-KG) based on cross-modal knowledge injection. Specifically, by integrating the heterogeneous Mamba with the Transformer in both parallel and sequential manners, we construct the spatially enhanced dual-stream state-space module (S-DSSM) and the temporally enhanced dual-stream state-space module (T-DSSM) to strengthen joint spatiotemporal modeling. Additionally, a knowledge graph that integrates both commonsense and dataset-specific information is constructed and adaptively injected into the decoder to furnish the model with extensive video-related knowledge. Experimental results indicate that the structural designs of DSSM-KG, together with the knowledge injection mechanism, demonstrate significant efficacy, yielding competitive performance on mainstream video captioning datasets such as MSVD and MSR-VTT.

In the domain of recommendations, previous works often retrieve items through sampling strategies from the database to gather negative signals for exploring implicit feedback. However, because of extremely sparse records, the existing items used as negative samples may not sufficiently support the interacted items in depicting the diverse interests of users. Consequently, the generation of negative samples needs to be explored in recommendation systems. In this study, we propose an interest-disentangled contrastive sample generation (IDCG) model to enhance interest modeling by contrasting interacted items with the generated samples for recommendation. Specifically, we decouple the interacted items of users into positively relevant and irrelevant factors of interest, providing a valuable clue to learn negatively relevant factors in personalized interests. Then, negative samples are generated by merging the learned negatively relevant factors and irrelevant factors. At this point, a two-level contrast is constructed between positive and negative samples and between the relevant factors of positives and negatives, providing auxiliary collaborative signals to debias and alleviate the interaction sparsity issue. Extensive experiments on three real datasets demonstrate the effectiveness of IDCG in generating targeted and meaningful negative samples from the perspective of disentangling relevant factors to promote interest modeling for recommendation.

To address the data sparsity issue, conventional graph-based models leverage structural signals from the interaction graph to embed users' interests. However, these models learn a uniform representation for interest modeling, which blends users' diverse intents and inevitably biases interest learning, hindering recommendations. Although the fine-grained paradigm can learn the intents of interactions separately to alleviate learning bias, the relationships among intents and the disentangled manner require elaborate design. Existing fine-grained models emphasize intent diversity and employ additional data splitting for disentanglement, which ignores the hierarchical relationship, exacerbates data sparsity, and increases the computational burden. To address these issues, we explore hierarchical intents and adaptive intent learning, proposing a hierarchical intent-based interest disentanglement (HIID) model for personalized recommendation. HIID introduces learnable intent queries to guide interest disentanglement from global interactions in a split-free manner. It raises a hierarchical intent hypothesis to involve hierarchical CF signals for interest modeling, where intents within the same level appear relatively diverse, and the in-depth intents are abstracted from the superficial ones. Both adaptive intent learning and hierarchical hypothesis help extract significant CF signals to promote personalized recommendation. Extensive experiments on public datasets show that the proposed HIID outperforms the state-of-the-art CF models for recommendation. Furthermore, HIID implements adaptive interest disentanglement in a split-free manner, improving the training efficiency of the recommender model compared to the existing fine-grained interest models.

The prevalent recommendation techniques explore the graph structure of interactions to alleviate the interaction sparsity issue for inferring users’ interests. These graph models focus on extracting local structural signals to model users’ interests, introducing grid-like distortion and ignoring the hierarchical tree-like structure when learning from the interaction graph. The learned interests lack significant hierarchical signals, resulting in suboptimal recommendation performance. In this article, we investigate geometric-augmented graph learning with hyperbolic and Euclidean geometries to delve into local structural and hierarchical knowledge from the interaction graph. A self-teaching network called geometric-augmented self-distillation (GASD) is proposed to transfer hierarchical knowledge from hyperbolic to Euclidean space. The transfer learning enables shrinking of the network into a primary student to implement effective and efficient inference in Euclidean space, preventing computational burden in hyperbolic space. Experiments on publicly available datasets demonstrate that the proposed GASD outperforms the state-of-the-art models, verifying the effectiveness and efficiency of knowledge transfer by self-distillation to aggregate knowledge adaptively for personalized recommendation.

Recently, recommender systems have witnessed the fast evolution of Internet services. However, it suffers hugely from inherent bias and sparsity issues in interactions. The conventional uniform embedding learning policies fail to utilize the imbalanced interaction clue and produce suboptimal representations to users and items for recommendation. Towards the issue, this work is dedicated to bias-aware embedding learning in a decomposed manner and proposes a counterfactual graph convolutional learning (CGCL) model for personalized recommendation. Instead of debiasing with uniform interaction sampling, we follow the natural interaction bias to model users’ interests with a counterfactual hypothesis. CGCL introduces bias-aware counterfactual masking on interactions to distinguish the effects between majority and minority causes on the counterfactual gap. It forms multiple counterfactual worlds to extract users’ interests in minority causes compared to the factual world. Concretely, users and items are represented with a causal decomposed embedding of majority and minority interests for recommendation. Experiments show that the proposed CGCL is superior to the state-of-the-art baselines. The performance illustrates the rationality of the counterfactual hypothesis in bias-aware embedding learning for personalized recommendation.

The information era brings both opportunities and challenges to information services. Confronting information overload, recommendation technology is dedicated to filtering personalized content to meet users’ requirements. The extremely sparse interaction records and their imbalanced distribution become a big obstacle to building a high-quality recommendation model. In this article, we propose a swarm self-supervised hypergraph embedding (SHE) model to predict users’ interests by hypergraph convolution and self-supervised discrimination. SHE builds a hypergraph with multiple interest clues to alleviate the interaction sparsity issue and performs interest propagation to embed CF signals in hybrid learning on the hypergraph. It follows an auxiliary local view by similar hypergraph construction and interest propagation to restrain unnecessary propagation between user swarms. Besides, interest contrast further inserts self-discrimination to deal with long-tail bias issue and enhance interest modeling, which aid recommendation by a multi-task learning optimization. Experiments on public datasets show that the proposed SHE outperforms the state-of-the-art models demonstrating the effectiveness of hypergraph-based interest propagation and swarm-aware interest contrast to enhance embedding for recommendation.

Previous works build interest learning via mining deeply on interactions. However, the interactions come incomplete and insufficient to support interest modeling, even bringing severe bias into recommendations. To address the interaction sparsity and the consequent bias challenges, we propose a graph contrastive learning on complementary embedding (GCCE), which introduces negative interests to assist positive interests of interactions for interest modeling. To embed interest, we design a perturbed graph convolution by preventing embedding distribution from bias. Since negative samples are not available in the general scenario of implicit feedback, we elaborate a complementary embedding generation to depict users’ negative interests. Finally, we develop a new contrastive task to contrastively learn from the positive and negative interests to promote recommendation. We validate the effectiveness of GCCE on two real datasets, where it outperforms the state-of-the-art models for recommendation.

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the "cold-start" problem for matchmaking rating algorithms.

With the rapid evolution of social networks, the increasing user intention gap and visual semantic gap both bring great challenge for users to access satisfied contents. It becomes promising to investigate users' customized multimedia recommendation. In this paper, we propose cross-modal collaborative manifold propagation (CMP) for image recommendation. CMP leverages users' interest distribution to propagate images' user records, which lets users know the trend from others and produces interest-aware image candidates upon users' interests. Visual distribution is investigated simultaneously to propagate users' visual records along dense semantic visual manifold. Visual manifold propagation helps to estimate semantic accurate user-image correlations for the candidate images in recommendation ranking. Experimental performance demonstrate the collaborative user-image inferring ability of CMP with effective user interest manifold propagation and semantic visual manifold propagation in personalized image recommendation.