Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering

Jia, Zhaohong; Li, Jin; Zhang, Yiwen; Liu, Chuang; Li, Kai; Yang, Yun

doi:10.1109/tcss.2022.3217277

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…The improvements of ARRQP over the other methods for each case (the methods are divided based on the anomalies addressed by them) are shown in the tables in bold. (ii) Comparison of prediction accuracy between ARRQP and DCLG: It may be noted from Table 4, the performance of ARRQP degraded by −1.97% compared to DCLG [32] in terms of MAE on TP-5. However, the performance of ARRQP improved by 8.34% for the same in terms of RMSE.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…(i) Utilizing outlierresilient loss functions: Some methods employ specialized loss functions that are resilient to the influence of outliers. These loss functions, such as L1 loss [22], [27], [54], Huber loss [25], [31], [32], Cauchy loss [8], [23], have been demonstrated to be more effective than the standard L2 loss function when dealing with outliers. These robust loss functions downweight the impact of outliers during the training process, allowing the model to focus more on learning from the majority of the data.…”

Section: Presence Of Outliersmentioning

confidence: 99%

“…Notably, our framework demonstrates its efficacy in detecting and effectively handling all the mentioned anomalies. [3], [38], [39] [7], [9], [29], [40], [41], [42], [43], [44], [45], [46], [62] ✓ ✓ [12], [15], [28], [30], [33], [47], [48], [49], [50], [51] [58], [59] [25], [27], [32], [54] ✓…”

Section: Cold Startmentioning

confidence: 99%

“…Nevertheless, the learningbased methods that heavily rely on contextual features [18], [27], [28], [29], [30] for QoS prediction may struggle to achieve desirable prediction accuracy when collaborative QoS features, which are often more relevant, are absent or not adequately considered. To further enhance prediction accuracy, recent techniques in the literature have aimed to address anomalies in QoS prediction, including handling outliers using robust outlier-resilient loss functions [8], [22], [25], [31], [32] and addressing the cold start problem [33]. However, many of these methods tend to focus on a subset of these challenges and may fall short when multiple issues coexist simultaneously, leading to performance degradation.…”

Section: Introductionmentioning

confidence: 99%

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TRQP: Trust-Aware Real-Time QoS Prediction Framework Using Graph-Based Learning

Kumar

Chattopadhyay

2022

Service-Oriented Computing

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In the realm of modern service-oriented architecture, ensuring Quality of Service (QoS) is of paramount importance. The ability to predict QoS values in advance empowers users to make informed decisions, ensuring that the chosen service aligns with their expectations. This harmonizes seamlessly with the core objective of service recommendation, which is to adeptly steer users towards services tailored to their distinct requirements and preferences. However, achieving accurate and real-time QoS predictions in the presence of various issues and anomalies, including outliers, data sparsity, grey sheep instances, and cold start scenarios, remains a challenge. Current state-of-the-art methods often fall short when addressing these issues simultaneously, resulting in performance degradation. In response, in this paper, we introduce an anomaly-resilient real-time QoS prediction framework (called ARRQP). Our primary contributions encompass proposing an innovative approach to QoS prediction aimed at enhancing prediction accuracy, with a specific emphasis on improving resilience to anomalies in the data. ARRQP utilizes the power of graph convolution techniques, a powerful tool in graph-based machine learning, to capture intricate relationships and dependencies among users and services. By leveraging graph convolution, our framework enhances its ability to model and seize complex relationships within the data, even when the data is limited or sparse. ARRQP integrates both contextual information and collaborative insights, enabling a comprehensive understanding of user-service interactions. By utilizing robust loss functions, this approach effectively reduces the impact of outliers during the training of the predictive model. Additionally, we introduce a method for detecting grey sheep users or services that is resilient to sparsity. These grey sheep instances are subsequently treated separately for QoS prediction. Furthermore, we address the cold start problem as a distinct challenge by emphasizing contextual features over collaborative features. This approach allows us to effectively handle situations where newly introduced users or services lack historical data. Experimental results on the publicly available benchmark WS-DREAM dataset demonstrate the framework's effectiveness in achieving accurate and timely QoS predictions, even in scenarios where anomalies abound.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%