Service Recommendation

Joint QoS prediction 

Dependable service-oriented computing relies on multiple Quality of Service (QoS) parameters that are essential to assess service optimality. However, real-world QoS data are extremely sparse, noisy, and shaped by hierarchical dependencies arising from QoS interactions, and geographical and network-level factors, making accurate QoS prediction challenging. Existing methods often predict each QoS parameter separately, requiring multiple similar models, which increases computational cost and leads to poor generalization. Although recent joint QoS prediction studies have explored shared architectures, they suffer from negative transfer due to loss-scaling caused by inconsistent numerical ranges across QoS parameters and further struggle with inadequate representation learning, resulting in degraded accuracy. This paper presents an unified strategy for joint QoS prediction, called SHARP-QoS, that addresses these issues using three components. First, we introduce a dual mechanism to extract the hierarchical features from both QoS and contextual structures via hyperbolic convolution formulated in the Poincaré ball. Second, we propose an adaptive feature-sharing mechanism that allows feature exchange across informative QoS and contextual signals. A gated feature fusion module is employed to support dynamic feature selection among structural and shared representations. Third, we design an EMA-based loss balancing strategy that allows stable joint optimization, thereby mitigating the negative transfer. Evaluations on three datasets with two, three, and four QoS parameters demonstrate that SHARP-QoS outperforms both single- and multi-task baselines. Extensive study shows that our model effectively addresses major challenges, including sparsity, robustness to outliers, and cold-start, while maintaining moderate computational overhead, underscoring its capability for reliable joint QoS prediction. 

References:

SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for joint Prediction of QoS

Time-aware QoS prediction 

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, achieving high prediction accuracy and faster responsiveness. We combine 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. 

References:

1. TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features

2. HCTN: Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network

Trust-aware QoS prediction 

QoS prediction algorithm requires to be real-time to be integrated with most real-time service recommendation or composition algorithms. However, real-time algorithms are prone to compromise on the solution quality to improve their responsiveness, which we aim to address in our work. The collaborative filtering (CF) technique, the most widely used QoS prediction method, considers the influences of all users/services while predicting the QoS value for a given target user-service pair. However, the presence of untrustworthy users/services, whose QoS invocation patterns are different from the rest, may lead to degradation in prediction accuracy. Moreover, in many cases, the quality of the prediction algorithms often deteriorates to ensure faster responsiveness due to their inability to capture non-linear, higher-order, and complex relationships among user-service QoS data. We propose a trust-aware QoS prediction framework leveraging a novel graph-based learning approach. Our framework is competent enough to identify trustworthy users and services while learning effective feature representation for finding a rich collaborative signal in an end-to-end fashion. 

References:

1. TRQP: Trust-aware Real-time QoS Prediction Framework using Graph-based Learning

2. ARRQP: Anomaly Resilient Real-time QoS Prediction Framework with Graph Convolution 

Scalable QoS Prediction

With the increasing trend of web services over the Internet, developing a robust Quality of Service (QoS) prediction algorithm for recommending services in real-time is becoming a challenge today. Designing an efficient QoS prediction algorithm achieving high accuracy, while supporting faster prediction to enable the algorithm to be integrated into a real-time system, is one of the primary focuses in the domain of Services Computing. The major state-of-the-art QoS prediction methods are yet to efficiently meet both criteria simultaneously, possibly due to the lack of analysis of challenges involved in designing the prediction algorithm. In our work, we systematically analyze the various challenges associated with the QoS prediction algorithm and propose solution strategies to overcome the challenges, and thereby propose a novel offline framework using deep neural architectures for QoS prediction to achieve our goals. Our framework, on the one hand, handles the sparsity of the dataset, captures the non-linear relationship among data, figures out the correlation between users and services to achieve desirable prediction accuracy. 

References: 

OffDQ: An Offline Deep Learning Framework for QoS Prediction