Recently, multimodal recommendation (MMRec) has received much attention, which models user preferences based on both user behaviors and modality information. Although current graph neural network based methods yield notable results in MMRec, certain limitations persist among these methods. 1) Most methods rely on pre-trained networks to extract modality features but fail to remove modality noise. 2) Recent methods leverage InfoNCE strategy to align representation, while ignoring the effect of feature redundancy and lacking sufficient alignment between different modality features. Such limitations ultimately harm the recommendation performance. To this end, we propose a Dual Mutual Information-Driven Multimodal Recommendation Model with Denoising Graph Autoencoder (DMIGA). Specifically, to reduce the noise within modality features, we design a denoising graph autoencoder with a cross-modal consistency constraint. Furthermore, we propose a dual mutual information learning mechanism on both feature and instance levels, to reduce the feature redundancy and align different representations. Experimental results on three real-world datasets consistently demonstrate that DMIGA outperforms state-of-the-art methods, with an average of 3.8% improvement.

With the vast range of available services, it has become an important challenge to recommend the optimal service for mashup developer. Recent studies are mainly limited by the service similarity, resulting in challenges such as discrepancy in textual semantics, implicity of inter-service relationships, and the sparsity of historical interactions. Service bundles, which offer a set of services, present a novel approach to address the mashup development problem. In this work, we propose an innovative message-passing model, a Heterogeneous Graph AutoEncoderbased service bundle recommendation model (HGAE), to tackle the issues. Specifically, we introduce the Graph Propagation Module to encode potentially implicit semantic relations in the Mashup-Service-Bundle heterogeneous graph. Furthermore, we build a unified representation for the bundle in the Bundle Prediction Module by combining an autoencoder and spatial attention mechanism, enabling the integration of relationships across different node and edge types. Extensive experiments on real-world datasets demonstrate that HGAE notably outperforms state-of-the-art methods on all metrics, with improvements of 8.69% in NDCG and 9.55% in Recall on the ProgrammableWeb dataset.

Multivariate time series (MTS) data in real-world scenarios are often incomplete, which hinders effective data analysis. Therefore, MTS imputation has been widely studied to facilitate various MTS tasks. Existing imputation methods primarily initialize missing values with zeros in order to perform effective incomplete MTS encoding, which impede the model's capacity to precisely discern the missing distribution. Moreover, these methods often overlook the global similarity in time series but are limited in the use of local information within the sample. To this end, we propose a novel multivariate time series imputation network model, named MMNet. MMNet introduces a Missing-Aware Embedding (MAE) approach to adaptively represent incomplete MTS, allowing the model to better distinguish between missing and observed data. Furthermore, we design a Memory-Enhanced Encoder (MEE) aimed at modeling prior knowledge through memory mechanism, enabling better utilization of the global similarity within the time series. Building upon this, MMNet incorporates a Multi-scale Mixing architecture (MSM) that leverages information from multiple scales to enhance the final imputation. Extensive experiments on four public real-world datasets demonstrate that, MMNet yields a more than 25% gain in performance, compared with the state-of-the-art methods.

Nonintrusive load monitoring (NILM) has emerged as a pivotal technology in energy management, garnering significant attention in both research and engineering communities. Despite its potential, conventional NILM methods often exhibit limitations in addressing critical challenges such as domain shift, new appliance detection, and noise interference, thereby hindering their practical application. To overcome these limitations, we propose meta-learning named deep robust transfer neural network (RTNILM), a novel framework that simultaneously addresses these challenges while significantly enhancing NILM performance in real-world scenarios. The RTNILM framework incorporates several innovative components: first, an optimized deep, wide, and robust network architecture is derived through neural architecture search from source domain data set; second, pretrained with optimized general end-to-end loss to acquire a general appliance recognition ability and enhance the model’s robustness; third, further trained with model-agnostic meta-learning strategy to improve the model’s generalization on the target domain data set; fourth, by comparing the similarities between features from new appliances and known appliances, achieve new appliance detection. Extensive experimental evaluations across three public datasets and one self-collected dataset demonstrate the superiority of RTNILM in cross-domain recognition, new appliance detection, and noise interference. The average improvement in accuracy of cross domain appliance recognition, new appliance detection, and noise interference compared to other methods exceeded 10%, 20%, and 5%, respectively.

In the context of the development of the modern service industry, emerging technologies such as the Internet of Things (IoT) and 5G have promoted the integration of a large number of service devices, increasing the complexity of the service ecosystem and accelerating its evolution process. Although the service model has summarized the business relationship in the service ecosystem from the four aspects of workflow, data flow, resource flow and value flow, it has not formed a systematic description of the heterogeneous devices where the service is deployed. Therefore, future service ecosystem modeling methods need to solve the following two problems: how to describe heterogeneous devices to provide guidance for the deployment of services, and how to enable the service ecosystem to adapt to the dynamic adjustment of services.In this paper, in order to solve the problem of dynamic addition and deletion of services and dynamic replacement of deployment carriers, we propose a service deployment description method(SDDM) based on holon concept, and integrate it with service pattern, then extend service pattern description language , namely carrier SPDL (SPDL-C). To validate our framework, we empirically conducted a case study in which we selected an intelligent warehouse management service pattern as the object of study to reveal how our approach could address future challenges. Finally, we summarize and discuss the innovation and significance of the work.

Recommendation is a key mechanism for modern users to access items of their interests from massive entities and information. Recently, graph contrastive learning (GCL) has demonstrated satisfactory results on recommendation, due to its ability to enhance representation by integrating graph neural networks (GNNs) with contrastive learning. However, those methods often generate contrastive views by performing random perturbation on edges or embeddings, which is likely to bring noise in representation learning. Besides, in all these methods, the degree of user preference on items is omitted during the representation learning process, which may cause incomplete user/item modeling. To address these limitations, we propose the Adaptive Fusion of Multi-View Graph Contrastive Recommendation (AMGCR) model. Specifically, to generate the informative and less noisy views for better contrastive learning, we design four view generators to learn the edge weights focusing on weight adjustment, feature transformation, neighbor aggregation, and attention mechanism, respectively. Then, we employ an adaptive multi-view fusion module to combine different views from both the view-shared and the view-specific levels. Moreover, to make the model capable of capturing preference information during the learning process, we further adopt a preference refinement strategy on the fused contrastive view. Experimental results on three real-world datasets demonstrate that AMGCR consistently outperforms the state-of-the-art methods, with average improvements of over 10% in terms of Recall and NDCG. Our code is available on https://github.com/Du-danger/AMGCR.

With the growth of cloud computing, a large number of innovative mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed for Mashup creation, but most either treat different feature factor interactions equally or solely rely on requirements for API recommendation. These approaches face several challenges such as API compatibility dependencies, ambiguous definition and boundary dilemmas of APIs, and sparse API invocation records. In this work, we propose a Semantic-Enhanced Heterogeneous Graph Network(SEHGN) for Mashup creation. To address the above deficiencies, we design a multi-semantic aggregator to capture semantic associations between features to encode multiple node-edge relationships. Then, we introduce a semantic embedding component to generate text embedding vectors for mashups and APIs to learn global and local semantic information about text documents at different levels of abstraction. Finally, we fuse the output vectors to obtain a list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that SEHGN outperforms state-of-the-art models in terms of overall and long-tail Web API recommendations.

In this paper, we present the design of JTang HSS (JTang Healthcare Service Platform for the Senior), a healthcare service platform for the senior based on cloud computing technology. In our system, services provided by third parties such as hospitals, physical examination centers, communities and some other institutions, can be integrated, managed and optimized seamlessly. The platform contains three subsystems, which are health services bus platform (HSB), Middleware Platform (MP) and health services library (HSL). HSB is constructed to facilitate data access for third parties. Meanwhile, we build the health data center to recommend services in a personalized manner based on middleware platform. Further, external services and platform owned services are orchestrated and integrated to a health services library, which supports an end-to-end process and supplies application services for the senior. The goal of the platform is to provide overall management and sufficient services in all aspects of healthcare of the senior. In detail, application services mainly include data collecting, health status assessing, entertainment supplying, social interaction, etc. Most of the services are provided as mobile applications running in devices like mobile phones and tablet computers.