In location-based social networks, next point of interest (POI) recommendation predicts the next-visited POIs of users by mining their behavioral patterns. However, existing POI recommendation methods based on graph neural networks and attention mechanisms fail to adequately capture: 1) the local structural features influenced by the trajectories of other users (i.e., the relationships between POIs visited by users); and 2) the dynamic visitation preferences and channel relationships among POIs (i.e., interdependencies between contextual features). We propose a next-POI recommendation model based on graph structure and sequential pattern to address these limitations. The model generates graph representations that reflect the real-time preferences of users by extracting local structures from a global POI graph. In addition, we design a temporal-aware self-attentive graph convolutional network and a channel attention mechanism to capture the structural features of users’ sequential visitation tendencies and the latent feature-channel relationships between POIs, respectively. These components enhance the ability of the model to characterize dynamic user preferences and behavioral changes. The results demonstrate that our model outperforms baseline methods on three real-world datasets, validating its effectiveness in capturing both global and local information.

Graph Neural Networks (GNNs) have been widely adopted in recommendation systems. When integrated into a federated learning framework, GNNs can enhance the model’s expressive capability. However, challenges arise in personalized representation and graph expansion due to the heterogeneity and locality of user data in federated recommendation systems. To address these challenges, we propose a federated recommendation model based on personalized attention and privacy-preserving dynamic graphs. The method first matches neighbor users for each selected client. Subsequently, it counts the interaction frequencies of items for both local and neighbor users to construct personalized weights, which captures the unique characteristics of different users. Additionally, we designs a method for constructing privacy-preserving dynamic graphs. In each round of federated training, the selected client adds pseudo-interaction items to its own interaction subgraph, perturbing the real interactions. After completing local training, the noisy interaction subgraph is incorporated into the global graph to capture higher-order connectivity information among users while safeguarding their interaction privacy. We conduct extensive experiments on three benchmark datasets, and the results demonstrate that the proposed PADG method achieves superior performance while effectively protecting privacy.

Most current session-based recommendations model session sequences solely based on the user's target behavior, ignoring the user's hidden preferences in auxiliary behaviors. Additionally, they use ordinary graphs to model one-to-one item correlations in the current session and fail to leverage other sessions to learn richer higher-order item correlations. To address these issues, a multi-behavior hypergraph contrastive learning model for session-based recommendations is proposed. This model represents all the sessions as global hypergraphs according to two types of behavior sequences. It employs contrastive learning to obtain global item embeddings, which are further aggregated to generate a global session representation that captures higher-order correlations of items from all session perspectives. A novel local heterogeneous hypergraph is designed for the current session to capture higher-order correlations between items with different behaviors in the current session, thus enhancing the local session representation. Additionally, a novel self-supervised signal is created by constructing a multi-behavior line graph, enhancing the global session representation. Finally, the local session representation, global session representation, and global item embedding are used to learn the predicted interaction probability of each item. Extensive experiments are conducted on three real datasets, and the results demonstrate that the proposed model significantly improves recommendation accuracy.

The temporal decay of trust data in dynamic edge computing environments leads to inaccurate evaluation, and the recommended trust values from heterogeneous nodes are affected by subjective bias and are vulnerable to malicious attacks. To address these issues, this paper proposes a novel lightweight dynamic trust evaluation model. First, a time decay function is derived based on Newton’s law of cooling to effectively reflect the impact of trust timeliness on trust evaluation. On this basis, the real value is iteratively calculated and used as the recommended trust value through the truth discovery algorithm, enhancing the accuracy of trust evaluation. Then, the weight of the recommended trust value in the comprehensive trust value is determined based on the standard deviation of the weight of perceived data from each recommending node, balancing the influence of subjective and objective factors on trust evaluation results. Lastly, the comprehensive trust value is dynamically weighted, and an incentive mechanism is employed to update trust data based on feedback, reflecting the dynamic nature of trust. Theoretical analysis demonstrates that the model presented in this paper exhibits low time and space complexities, meeting the lightweight requirements of the edge computing environment. Experimental results indicate high recommendation trust accuracy and interaction success rates, as well as effective resistance against malicious attacks.

To address the problem of data sparsity in recommendation systems, various studies have used knowledge graphs as auxiliary information. These studies have employed multitask learning (MTL) to enhance recommendation performance. However, the shared information between tasks is not fully explored when using an MTL strategy for training both recommendation and knowledge graph-related tasks. Moreover, most studies cannot effectively model the knowledge sharing, consequently affecting recommendation performance. In response to these problems, we proposed a novel knowledge graph-based personalized multitask enhanced recommendation model. To explore the shared information between tasks, a relation attention mechanism was proposed to distinguish the relative importance of neighborhood information to the central entity. Additionally, we utilized a lightweight graph convolutional network to more effectively aggregate high-order neighborhood information from the knowledge graph. This approach improves the accuracy of neighborhood feature and ensures that more suitable shared information is obtained. Furthermore, we developed a linear interaction component to model knowledge sharing between recommendation and knowledge graph embedding tasks. This component allows for detailed feature interaction learning between items and entities, enhancing the shared feature representation, generalization capabilities, and overall performance of the recommendation system. The experimental results on three public datasets indicate that our model outperforms other benchmark models in CTR prediction and top-$\boldsymbol{K}$recommendation.

Sequential recommendation models analyze users’ historical interactions to predict the next item they will en gage with. In order to better capture users’ dynamic interest preferences, most existing sequential recommendation models that introduce heterogeneous time intervals lead to increased model complexity, which raises computational costs and training difficulty. This is particularly evident in long sequential data, where the model need to handle a large variety of different time intervals. Additionally, accurately modeling the impact of long time intervals on user behavior remains a significant challenge. To address these issues, we propose a lightweight sequential recommendation algorithm with time interval awareness augmen tation (TALSAN). This model introduces a novel uniform data augmentation operator to improve the distribution of original data samples and employs a time-aware self-attention layer to model user interactions, maintaining the continuity of the original sequence. By integrating temporal context with posi tional features, TALSAN constructs a streamlined self-attention network for predicting user behavior. Comparative testing on datasets such as ML-100K, ML-1M, Amazon Beauty, Amazon Toys, and Amazon Fashion demonstrates the model’s superiority over existing baselines. Our results confirm that TALSAN not only mitigates cold start issues but also enhances the ability to learn user preferences, leading to improved prediction accuracy.

The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rating, item average rating, user average rating, and MF, which improves the prediction accuracy. Then, based on the above algorithm, this article proposes a MF RS for preserving user privacy by using local differential privacy technology. In this algorithm, the rating data are normalized on the user side to reduce global sensitivity. Then, Laplace noise is added to sensitive data before it is sent to the aggregator. Finally, based on the disturbed data, rating prediction is realized by using the MF algorithm. The proposed method is compared with five well-known recommendation methods on four public datasets. The experimental results show that the proposed algorithm achieves better recommendation performance at the same privacy-preserving level.

In view of the lack of research regarding characteristic factors (e.g., service cost, quality, etc.) in trust models for cloud environments, as well as the lack of negotiation and monitoring mechanisms, a trust model based on characteristic factors and service level agreements (SLAs) is proposed in this article. First, on the basis of comprehensive trust and self-recommended trust, we introduce the cost deviation trust and service quality coefficient, to simultaneously consider the influence of common and characteristic factors upon trust evaluations and thereby improve the accuracy of trust evaluations. Second, we establish a negotiation and monitoring mechanism whereby both parties sign an SLA before the trade and require SLA agent monitoring services to improve the accuracy of the service cost and quality evaluation and the efficiency of malicious entity identification. Finally, using the agreement quality, experience quality, and monitoring quality, we more accurately judge the trade result and update the recognition degree (which plays a key role in trust evaluation), to further improve the accuracy of trust evaluation and thereby the trade success rate. The results of experiments conducted upon real datasets show that our model can effectively resist spoofing, coordination, and defamation attacks from malicious entities, and it offers a high trade success rate.

Abstract Spatial query is an important supporting technology in the Internet of Things (IoT) and location‐based services (LBS). The k‐nearest neighbor query is widely used for spatial queries. However, user location privacy may be leaked in the query. In addition, some users are malicious or uncooperative. With the objective of overcoming these problems, a k‐nearest neighbor query method based on trust and location privacy protection is proposed. First, we employ a new K‐anonymity method based on cooperation to protect a query user's location privacy. In this method, the query user constructs an anonymous group by introducing a trust mechanism to incentivize cooperation among users. Then, according to the different radii of the selection area set by the query user, agent users with higher reputation values who send query requests for the query user are selected. Finally, the agent users obtain the query results from the LBS server and forward them to the query user, and the query user screens the results according to his or her real location. The experiments show that our method can effectively stimulate users to cooperate, better exclude malicious users, and improve the accuracy of the query results while protecting the privacy of the query user.

Abstract Aiming at overcoming problems such as malicious entities and trust crises in cloud environments, a trust management model based on mutual trust and a reward‐with‐punishment mechanism is proposed in this paper. First, according to reputation values of entities and trust relationships among entities, we calculate the comprehensive trust values of a service request user to several candidate service providers and probe these providers with high comprehensive trust values. Second, we select a trade provider according to mutual trust. Finally, after the trade, we update the reputation values and other data of the request user, trade provider, and recommenders based on the final trade result to reward or punish them to various degrees, thus ultimately reducing malicious or dishonest behavior. The experimental results show that our model can effectively identify malicious entities to increase the trade success rate.