406 research outputs found
A Dual Latent State Learning Approach: Exploiting Regional Network Similarities for QoS Prediction
Individual objects, whether users or services, within a specific region often
exhibit similar network states due to their shared origin from the same city or
autonomous system (AS). Despite this regional network similarity, many existing
techniques overlook its potential, resulting in subpar performance arising from
challenges such as data sparsity and label imbalance. In this paper, we
introduce the regional-based dual latent state learning network(R2SL), a novel
deep learning framework designed to overcome the pitfalls of traditional
individual object-based prediction techniques in Quality of Service (QoS)
prediction. Unlike its predecessors, R2SL captures the nuances of regional
network behavior by deriving two distinct regional network latent states: the
city-network latent state and the AS-network latent state. These states are
constructed utilizing aggregated data from common regions rather than
individual object data. Furthermore, R2SL adopts an enhanced Huber loss
function that adjusts its linear loss component, providing a remedy for
prevalent label imbalance issues. To cap off the prediction process, a
multi-scale perception network is leveraged to interpret the integrated feature
map, a fusion of regional network latent features and other pertinent
information, ultimately accomplishing the QoS prediction. Through rigorous
testing on real-world QoS datasets, R2SL demonstrates superior performance
compared to prevailing state-of-the-art methods. Our R2SL approach ushers in an
innovative avenue for precise QoS predictions by fully harnessing the regional
network similarities inherent in objects
TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features
Recently, with the rapid deployment of service APIs, personalized service
recommendations have played a paramount role in the growth of the e-commerce
industry. Quality-of-Service (QoS) parameters determining the service
performance, often used for recommendation, fluctuate over time. Thus, the QoS
prediction is essential to identify a suitable service among functionally
equivalent services over time. The contemporary temporal QoS prediction methods
hardly achieved the desired accuracy due to various limitations, such as the
inability to handle data sparsity and outliers and capture higher-order
temporal relationships among user-service interactions. Even though some recent
recurrent neural-network-based architectures can model temporal relationships
among QoS data, prediction accuracy degrades due to the absence of other
features (e.g., collaborative features) to comprehend the relationship among
the user-service interactions. This paper addresses the above challenges and
proposes a scalable strategy for Temporal QoS Prediction using Multi-source
Collaborative-Features (TPMCF), achieving high prediction accuracy and faster
responsiveness. TPMCF combines the collaborative-features of users/services by
exploiting user-service relationship with the spatio-temporal auto-extracted
features by employing graph convolution and transformer encoder with multi-head
self-attention. We validated our proposed method on WS-DREAM-2 datasets.
Extensive experiments showed TPMCF outperformed major state-of-the-art
approaches regarding prediction accuracy while ensuring high scalability and
reasonably faster responsiveness.Comment: 10 Pages, 7 figure
Web service QoS prediction using improved software source code metrics
Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services' QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics
Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction
Quality of Service (QoS) prediction is an essential task in recommendation
systems, where accurately predicting unknown QoS values can improve user
satisfaction. However, existing QoS prediction techniques may perform poorly in
the presence of noise data, such as fake location information or virtual
gateways. In this paper, we propose the Probabilistic Deep Supervision Network
(PDS-Net), a novel framework for QoS prediction that addresses this issue.
PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate
layers and learns probability spaces for both known features and true labels.
Moreover, PDS-Net employs a condition-based multitasking loss function to
identify objects with noise data and applies supervision directly to deep
features sampled from the probability space by optimizing the Kullback-Leibler
distance between the probability space of these objects and the real-label
probability space. Thus, PDS-Net effectively reduces errors resulting from the
propagation of corrupted data, leading to more accurate QoS predictions.
Experimental evaluations on two real-world QoS datasets demonstrate that the
proposed PDS-Net outperforms state-of-the-art baselines, validating the
effectiveness of our approach
A Location-sensitive and Network-aware Broker for Recommending Web Services
Collaborative Filtering (CF) is one of the renowned recommendation techniques that can be used for predicting unavailable Quality-of-Service (QoS) values of Web services. Although several CF-based approaches have been proposed in recent years, the accuracy of the QoS values, that these approaches provide, raises some concerns and hence, could undermine the real ”quality” of Web services. To address these concerns, context information such as communication-network configuration and user location could be integrated into the process of developing recommendations. Building upon such context information, this paper proposes a CF-based Web Services recommendation approach, which incorporates the effect of locations of users, communication-network configurations of users, andWeb services run-time environments on the recommendations. To evaluate the accuracy of the recommended Web services based on the defined QoS values a set of comprehensive experiments are conducted using a real dataset of Web services. The experiments are in line with the importance of integrating context into recommendations
Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model
The functionally equivalent web services (WSs) with different quality of service (QoS) leads to WS discovery models to identify the optimal WS. Due to the unpredictable network connections and user environment, the predicted values of the QoS are likely to fluctuate. The proposed Context-Aware Bayesian Network (CABN) system overcomes these limitations by incorporating the contextual factors in user, server, and environmental perspective. In this paper, three components are introduced for personalized QoS prediction. First, the CABN incorporates the pre-clustering model and reduces the searching space for QoS prediction. Second, the CABN confronts with the multi-constraint problem while considering the multi-dimensional QoS parameters of similar QoS data in WS discovery. Third, the CABN sends the normalized QoS value of records in similar as well as neighbor clusters as inputs to the Dynamic Bayesian Network and improves the prediction accuracy. The experimental results prove that the proposed CABN achieves better WS-Discovery than the existing work within a reasonable time
Composition de services basée sur les relations sociales entre objets dans l’IoT Service composition based on social relations between things in IoT
With the rapid development of service-oriented computing applications and social Internet ofthings (SIoT), it is becoming more and more difficult for end-users to find relevant services to create value-added composite services in this big data environment. Therefore, this work proposes S-SCORE (Social Service Composition based on Recommendation), an approach for interactive web services composition in SIoT ecosystem for end-users. The main contribution of this work is providing a novel recommendation approach, which enables to discover and suggest trustworthy and personalized web services that are suitable for composition. The first proposed model of recommendation aims to face the problem of information overload, which enables to discover services and provide personalized suggestions for users without sacrificing the recommendation accuracy. To validate the performance of our approach, seven variant algorithms of different approaches (popularity-based, user-based and item-based) are compared using MovieLens 20M dataset. The experiments show that our model improves the recommendation accuracy by 12% increase with the highest score among compared methods. Additionally it outperforms the compared models in diversity over all lengths of recommendation lists. The second proposed approach is a novel recommendation mechanism for service composition, which enables to suggest trustworthy and personalized web services that are suitable for composition. The process of recommendation consists of online and offline stages. In the offline stage, two models of similarity computation are presented. Firstly, an improved users’ similarity model is provided to filter the set of advisors for an active user. Then, a new service collaboration model is proposed that based on functional and non-functional features of services, which allows providing a set of collaborators
for the active service. The online phase makes rating prediction of candidate services based on a hybrid algorithm that based on collaborative filtering technique. The proposed method gives considerable improvement on the prediction accuracy. Firstly, it achieves the lowest value in MAE (Mean Absolute Error) metric and the highest coverage values than other compared traditional collaborative filtering-based prediction approaches
A Holistic Approach for Collaborative Workload Execution in Volunteer Clouds
The demand for provisioning, using, and maintaining distributed computational resources is growing hand in hand with the quest for ubiquitous services. Centralized infrastructures such as cloud computing systems provide suitable solutions for many applications, but their scalability could be limited in some scenarios, such as in the case of latency-dependent applications. The volunteer cloud paradigm aims at overcoming this limitation by encouraging clients to offer their own spare, perhaps unused, computational resources. Volunteer clouds are thus complex, large-scale, dynamic systems that demand for self-adaptive capabilities to offer effective services, as well as modeling and analysis techniques to predict their behavior. In this article, we propose a novel holistic approach for volunteer clouds supporting collaborative task execution services able to improve the quality of service of compute-intensive workloads. We instantiate our approach by extending a recently proposed ant colony optimization algorithm for distributed task execution with a workload-based partitioning of the overlay network of the volunteer cloud. Finally, we evaluate our approach using simulation-based statistical analysis techniques on a workload benchmark provided by Google. Our results show that the proposed approach outperforms some traditional distributed task scheduling algorithms in the presence of compute-intensive workloads
Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information
Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this paper, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, to model the multi dimensional contextual information. Tensor Factorization naturally extends Matrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users’ check-ins instead of the traditional two dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data which is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach
Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches
In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work
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