The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals; (2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests.

Session-based recommendation (SBR) has received increasing attention to predict the next item via extracting and integrating both global and local item-item relationships. However, there still exist some deficiencies in current works when capturing these two kinds of relationships. For global item-item relationships, the global graph constructed by most SBR is a pseudo-global graph, which may cause redundant mining of sequence relationships. For local item-item relationships, conventional SBR only mines the sequence patterns while ignoring the feature patterns, which may introduce noise when learning users' interests. To address these problems, we propose a novel Knowledge-enhanced Multi-View Graph Neural Network (KMVG) by constructing three views, namely knowledge view, session view, and pairwise view. Specifically, benefiting from the rich semantic information in the knowledge graph (KG), we build a genuine global graph that is sequence-independent based on KG to mine the global item-item relationships in the knowledge view. Then, a session view is utilized to capture the contextual transitions among items as the sequence patterns of local item-item relationships, and a pairwise view is used to explore the feature commonality within a session as the feature patterns of the local item-item relationships. Extensive experiments on three real-world public datasets demonstrate the superiority of KMVG, showing that it outperforms the state-of-the-art baselines. Further analysis also reveals the effectiveness of KMVG in exploiting the item-item relationships under multiple views.

With the advances in geo-positioning technologies and location-based services, it is nowadays quite common for road networks to have textual contents on the vertices. Previous work on identifying an optimal route that covers a sequence of query keywords has been studied in recent years. However, in many practical scenarios, an optimal route might not always be desirable. For example, a personalized route query is issued by providing some clues that describe the spatial context between PoIs along the route, where the result can be far from the optimal one. Therefore, in this paper, we investigate the problem of clue-based route search (CRS), which allows a user to provide clues on keywords and spatial relationships. First, we propose a greedy algorithm and a dynamic programming algorithm as baselines. To improve efficiency, we develop a branch-and-bound algorithm that prunes unnecessary vertices in query processing. In order to quickly locate candidate, we propose an AB-tree that stores both the distance and keyword information in tree structure. To further reduce the index size, we construct a PB-tree by utilizing the virtue of 2-hop label index to pinpoint the candidate. Extensive experiments are conducted and verify the superiority of our algorithms and index structures.

With the proliferation of geo-textual objects on the web, extensive efforts have been devoted to improving the efficiency of top-k spatial keyword queries in different settings. However, comparatively much less work has been reported on enhancing the quality and usability of such queries. In this context, we propose means of enhancing the usability of a top-k group spatial keyword query, where a group of users aim to find k objects that contain given query keywords and are nearest to the users. Specifically, when users receive the result of such a query, they may find that one or more objects that they expect to be in the result are in fact missing, and they may wonder why. Specifically, we formalize the why-not query in relation to the top-k group spatial keyword query, called the Why-not Group Spatial Keyword Query (WGSK) that is able to provide a group of users with a more satisfactory query result. We propose a three-phase framework for efficiently computing the WGSK. Extensive experiments with real data offer evidence that the proposed solution excels over baselines with respect to both effectiveness and efficiency.

In Pickup-and-Delivery problems (PDP), mobile workers are employed to pick up and deliver items with the goal of reducing travel and fuel consumption. Unlike most existing efforts that focus on finding a schedule that enables the delivery of as many items as possible at the lowest cost, we consider trichromatic (worker-item-task) utility that encompasses worker reliability, item quality, and task profitability. Moreover, we allow customers to specify keywords for desired items when they submit tasks, which may result in multiple pickup options, thus further increasing the difficulty of the problem. Specifically, we formulate the problem of Online Trichromatic Pickup and Delivery Scheduling (OTPD) that aims to find optimal delivery schedules with highest overall utility. In order to quickly respond to submitted tasks, we propose a greedy solution that finds the schedule with the highest utility-cost ratio. Next, we introduce a skyline kinetic tree-based solution that materializes intermediate results to improve the result quality. Finally, we propose a density-based grouping solution that partitions streaming tasks and efficiently assigns them to the workers with high overall utility. Extensive experiments with real and synthetic data offer evidence that the proposed solutions excel over baselines with respect to both effectiveness and efficiency.

Several secure image encryption systems have been researched and formed by chaotic mechanisms in current decades. This work recommends an innovative quantum color image encryption method focused on the Lucas series-based substitution box to enhance the competence of encryption. The suggested encryption technique has more excellent key space and significant confidentiality. The chaotic system, along with the substitution box, exhibits additional complicated dynamical behavior, sufficient arbitrariness, and uncertainty than all others focused on just chaotic models. Theoretical and simulation assessments show that the offered image encryption performs admirably, its traditional equivalents in terms by efficiency in terms of statistical analysis.