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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.

Accurate traffic flow forecasting is crucial for intelligent transportation systems and relies on effectively modeling complex spatio-temporal dependencies. Although recent graph-based deep learning methods have achieved promising results, most focus on pairwise neighbor relationships, limiting their ability to capture higher-order spatio-temporal interactions in the traffic network. To overcome this limitation, we propose a novel Multi-source information driven Spatio-Temporal HyperGraph learning for traffic forecasting (MSTHG), which is designed to capture richer relational and semantic information. MSTHG introduces a multi-source hypergraph fusion strategy that jointly models dynamic high-order spatial and temporal correlations. Specifically, we build a spatial hypergraph based on geographical proximity to represent high-order spatial dependencies, and a temporal-trend hypergraph leveraging mutual information to capture nonlinear similarities among traffic series. To enhance the semantic richness of node representations, we integrate key daily and weekly information along with periodic features derived from Fast Fourier Transform (FFT). Following the obtained hypergraph, node representations are learned through a hypergraph convolutional network and subsequently processed by a GRU-MLP fusion module, which is designed to capture both local and global temporal dependencies. Extensive experiments on real-world benchmark datasets demonstrate that MSTHG outperforms state-of-the-art baselines. The source code is https://github.com/April-leng/MSTHG.git.

Addressing class-imbalanced graphs is a challenging task due to the involvement of both node attributes and graph structures. Existing works on class-imbalanced graphs simply assume that all node attributes are available. However, in real-world graphs, many nodes may lack attributes due to privacy issues or missing data, making class-imbalanced graph learning more challenging. In this paper, we propose GraphIAM, a novel two-stage algorithm for improving class-imbalanced node classification on attribute-missing graphs. In the pre-training phase, GraphIAM adopts graph contrastive learning with oversampling to tackle both attribute-missing and class-imbalanced issues. During fine-tuning, an adapter mechanism is introduced to learn node representations, alleviating the generalization gap between pre-training and downstream tasks. Experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance, outperforming class-imbalanced graph learning approaches by 5% in F Score on graphs with severe attribute missingness.

Knowledge graphs (KGs) consist of well-organized external information and have been proven to enhance recommendation quality effectively. Most KG-aware recommender systems are developed using real number space embeddings. In recent years, learning representations in the hypercomplex space has gained success and attention. Compared to single-component real-valued vectors, multi-component hypercomplex embeddings offer greater expressiveness, facilitating more meaningful modeling of users, items, entities, and their relations in the user-item interaction graph and KG. In this paper, we explore the integration of hypercomplex algebras in KG-aware recommendation and propose a Hypercomplex Knowledge Graph-aware Recommender (HKGR) method. Our HKGR models the interaction graph and KG in the hypercomplex space by utilizing specially designed hypercomplex graph neural networks. In particular, HKGR employs a hypercomplex attention-based aggregator to capture the structure and semantics of the KG. In the recommendation prediction phase, we design a hypercomplex interaction network that can approximate the high-order component interactions between users and items. Furthermore, we introduce a hypercomplex contrastive learning operator to strengthen cooperative signals between the interaction graph and KG modelings. Experiment results on the four real-world datasets show that our HKGR outperforms the state-of-the-art recommender baselines.

Graph-based social recommender systems utilize user-item interaction graphs and user-user social graphs to model user preferences. However, their performance can be limited by redundant and noisy information in these two graphs. Although several recommender studies on data denoising exist, most either rely on heuristic assumptions, which limit their adaptability, or use a single model that combines denoising and recommendation, potentially imposing substantial demands on the model capacity. To address these issues, we propose a dual Graph Denoising Social Recommender (GDSR), which consists of two steps: graph denoising and user preference prediction. First, we design a denoising module which exploits a dual diffusion model to alleviate noises in the interaction and social graphs by performing multi-step noise diffusion and removal. We develop three kinds of conditions to guide our dual graph diffusion paradigm and propose a cross-domain signal guidance mechanism to enhance the structure of denoised graphs. Second, we devise a recommender module that employs a dual graph learning structure on denoised graphs to generate recommendations. Moreover, we use additional supervision signals from the diffusion-enhanced data augmentation to introduce a graph contrastive learning task, enhancing the recommender module's representation quality and robustness. Experiment results show the effectiveness of our GDSR.

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure- and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets.

Recently, deep learning techniques have yielded immense success on recommender systems. However, one weakness of most deep methods is that, users/items mutual semantic relationships, which are latent in the user-item interactions, are not distilled out explicitly. Moreover, most methods have been primarily focused on representation learning in euclidean geometry. Since recent studies have shown that the bipartite graph structure has the non-euclidean latent anatomy, euclidean embeddings may suffer from a certain degree of distortion. In this work, we presentHyperbolicNeuralCollaborativeRecommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relationships among users/items for collaborative filtering tasks. HNCR first introduces a neighbor construction strategy to build user and item semantic neighborhoods. Then HNCR develops a framework based on deep learning and hyperbolic geometry to integrate constructed neighborhoods into recommendation. To evaluate our method, we conduct experiments on the four datasets. Experimental results show the superiority of HNCR compared with its euclidean counterpart and state-of-the-art recommendation baselines. The results also indicate that hyperbolic representations can reflect meaningful data insights.

Hypercomplex algebras are well-developed in the area of mathematics. Recently, several hypercomplex recommendation approaches have been proposed and yielded great success. However, two vital issues have not been well-considered in existing hypercomplex recommenders. First, these methods are only designed for specific and low-dimensional hypercomplex algebras (e.g., complex and quaternion algebras), ignoring the exploration and utilization of high-dimensional ones. Second, most recommenders treat every user-item interaction as an isolated data instance, without considering high-order collaborative relationships.