219 research outputs found
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
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
Prediction, Recommendation and Group Analytics Models in the domain of Mashup Services and Cyber-Argumentation Platform
Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very low API invocation from mashup applications creates a sparse mashup-web API dataset for the recommendation models to learn about the mashups and their web API invocation pattern. One research aims to analyze these mashup-specific critical issues, look for supplemental information in the mashup domain, and develop web API recommendation models for mashup applications. The developed recommendation model generates useful and accurate web APIs to reduce the impact of low API invocations in mashup application development.
Cyber-Argumentation platform also faces a similarly challenging issue. In large-scale cyber argumentation platforms, participants express their opinions, engage with one another, and respond to feedback and criticism from others in discussing important issues online. Argumentation analysis tools capture the collective intelligence of the participants and reveal hidden insights from the underlying discussions. However, such analysis requires that the issues have been thoroughly discussed and participant’s opinions are clearly expressed and understood. Participants typically focus only on a few ideas and leave others unacknowledged and underdiscussed. This generates a limited dataset to work with, resulting in an incomplete analysis of issues in the discussion. One solution to this problem would be to develop an opinion prediction model for cyber-argumentation. This model would predict participant’s opinions on different ideas that they have not explicitly engaged.
In cyber-argumentation, individuals interact with each other without any group coordination. However, the implicit group interaction can impact the participating user\u27s opinion, attitude, and discussion outcome. One of the objectives of this research work is to analyze different group analytics in the cyber-argumentation environment. The objective is to design an experiment to inspect whether the critical concepts of the Social Identity Model of Deindividuation Effects (SIDE) are valid in our argumentation platform. This experiment can help us understand whether anonymity and group sense impact user\u27s behavior in our platform. Another section is about developing group interaction models to help us understand different aspects of group interactions in the cyber-argumentation platform.
These research works can help develop web API recommendation models tailored for mashup-specific domains and opinion prediction models for the cyber-argumentation specific area. Primarily these models utilize domain-specific knowledge and integrate them with traditional prediction and recommendation approaches. Our work on group analytic can be seen as the initial steps to understand these group interactions
Network distance prediction for enabling service-oriented applications over large-scale networks
PublishedKnowledge of end-to-end network distances is essential to many service-oriented applications such as distributed content delivery and overlay network multicast, in which the clients have the flexibility to select their servers from among a set of available ones based on network distance. However, due to the high cost of global measurements in large-scale networks, it is infeasible to actively probe end-to-end network distances for all pairs. In order to address this issue, network distance prediction has been proposed by measuring a few pairs and then predicting the other ones without direct measurements, or splicing the path segments between each pair via observation. It is considered important to improve network performance, and enables service-oriented applications over large-scale networks. In this article, we first illustrate the basic ideas behind network distance prediction, and then categorize the current research work based on different criteria. We illustrate how different protocols work, and discuss their merits and drawbacks. Finally, we summarize our findings, and point out potential issues and future directions for further research
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the
most appropriate one among numerous functionally identical or similar service
candidates. Quality-of-Service (QoS) describes the non-functional
characteristics of Web services, and it has become the key differentiator for
service selection. However, users cannot invoke all Web services to obtain the
corresponding QoS values due to high time cost and huge resource overhead.
Thus, it is essential to predict unknown QoS values. Although various QoS
prediction methods have been proposed, few of them have taken outliers into
consideration, which may dramatically degrade the prediction performance. To
overcome this limitation, we propose an outlier-resilient QoS prediction method
in this paper. Our method utilizes Cauchy loss to measure the discrepancy
between the observed QoS values and the predicted ones. Owing to the robustness
of Cauchy loss, our method is resilient to outliers. We further extend our
method to provide time-aware QoS prediction results by taking the temporal
information into consideration. Finally, we conduct extensive experiments on
both static and dynamic datasets. The results demonstrate that our method is
able to achieve better performance than state-of-the-art baseline methods.Comment: 12 pages, to appear at the Web Conference (WWW) 202
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
Deep Learning Framework for Online Interactive Service Recommendation in Iterative Mashup Development
Recent years have witnessed the rapid development of service-oriented
computing technologies. The boom of Web services increases the selection burden
of software developers in developing service-based systems (such as mashups).
How to recommend suitable follow-up component services to develop new mashups
has become a fundamental problem in service-oriented software engineering. Most
of the existing service recommendation approaches are designed for mashup
development in the single-round recommendation scenario. It is hard for them to
update recommendation results in time according to developers' requirements and
behaviors (e.g., instant service selection). To address this issue, we propose
a deep-learning-based interactive service recommendation framework named DLISR,
which aims to capture the interactions among the target mashup, selected
services, and the next service to recommend. Moreover, an attention mechanism
is employed in DLISR to weigh selected services when recommending the next
service. We also design two separate models for learning interactions from the
perspectives of content information and historical invocation information,
respectively, as well as a hybrid model called HISR. Experiments on a
real-world dataset indicate that HISR outperforms several state-of-the-art
service recommendation methods in the online interactive scenario for
developing new mashups iteratively.Comment: 15 pages, 6 figures, and 3 table
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