Service Recommendation

Ongoing work:


FES: A Fast Efficient Scalable QoS Prediction Framework

Quality-of-Service (QoS) prediction of web service is an integral part of services computing due to its diverse applications in service composition/selection/recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm should be faster in terms of prediction time to be compatible with a real-time system. The other important factor to consider is scalability to tackle large-scale datasets. The existing QoS prediction algorithms often satisfy one goal while compromising the others. In this paper, we propose a semi-offline QoS prediction framework to achieve three important goals together: higher accuracy, faster prediction time, and scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework (FES) consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the proposed pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on WS-DREAM datasets show the efficiency (in terms of accuracy), scalability, and fast responsiveness (in terms of prediction time) of FES as compared to the state-of-the-art methods.


Reference:

  1. S. Chattopadhyay, C. Adak, R. R. Chowdhury “FES: A Fast Efficient Scalable QoS Prediction Framework”, arXiv. (link)


CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering

With the proliferation of Internet-of-Things and continuous growth in the number of web services at the Internet-scale, the service recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service recommendation is the Quality-of-Service (QoS) parameter, which depicts the performance of a web service. In general, the service provider furnishes the value of the QoS parameters during service deployment. However, in reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task, and thus, the QoS prediction has gained significant research attention. Multiple approaches are available in the literature for predicting service QoS. However, these approaches are yet to reach the desired accuracy level. In this paper, we study the QoS prediction problem across different users, and propose a novel solution by taking into account the contextual information of both services and users. Our proposal includes two key steps: (a) hybrid filtering, and (b) hierarchical prediction mechanism. On the one hand, the hybrid filtering method aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical prediction mechanism is to estimate the QoS value accurately by leveraging hierarchical neural-regression. We evaluated our framework on the publicly available WS-DREAM datasets. The experimental results show the outperformance of our framework over the major state-of-the-art approaches.

Publication:

  1. R. R. Chowdhury, S. Chattopadhyay, C. Adak, “CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering”, IEEE Trans. on Services Computing (TSC) 2020. (Accepted) (link)


QoS Value Prediction Using a Combination of Filtering Method and Neural Network Regression

With increasing demand and adoption of web services in the world wide web, selecting an appropriate web service for recommendation is becoming a challenging problem to address today. The Quality of Service (QoS) parameters, which essentially represent the performance of a web service, play a crucial role in web service selection. However, obtaining the exact value of a QoS parameter of service before its execution is impossible, due to the variation of the QoS parameter across time and users. Therefore, predicting the value of a QoS parameter has attracted significant research attention. In this paper, we consider the QoS prediction problem and propose a novel solution by leveraging the past information of service invocations. Our proposal, on one hand, is a combination of collaborative filtering and neural network-based regression model. Our filtering approach, on the other hand, is a coalition of the user-intensive and service-intensive models. In the first step of our approach, we generate a set of similar users on a set of similar services. We then employ a neural network-based regression module to predict the QoS value of a target service for a target user. The experiments are conducted on the WS-DREAM public benchmark dataset. Experimental results show the superiority of our method over state-of-the-art approaches.


Publication:

  1. S. Chattopadhyay, A. Banerjee, “QoS Value Prediction Using a Combination of Filtering Method and Neural Network Regression”, in the 17th International Conference on Service Oriented Computing (ICSOC), France, pp. 135 - 150, 2019. (link)


A Framework for Top Service Subscription Recommendations for Service Assemblers


It is common practice today for small and medium business houses to assemble and host services, than hosting everything themselves. To cater to diverse market needs, these houses often need to subscribe to different services from different information providers. The service contracts and the range of features and facilities supported and provided by the providers vary widely. A non-trivial challenge for a service assembler is in deciding the set of information providers to subscribe to, given the heterogeneity in the offerings provided, the economics of the business model, the target set of customers in the market place and most importantly, the profit margin. We present in this work, an automated framework that addresses this challenge and aids a service assembler with a cost-feature-performance balanced recommendation of the providers that can best serve his needs.


Publication:

S. Chattopadhyay, A. Banerjee, T. Mukherjee, “A Framework for Top Service Subscription Recommendations for Service Assemblers”, Proc. IEEE International Conference on Services Computing (SCC), San Francisco, pp. 332–339, 2016. (link)