A Time-Aware and Data Sparsity Tolerant Approach for Web Service Recommendation

Hu, Yan; Peng, Qimin; Hu, Xiaohui

doi:10.1109/icws.2014.18

Cited by 57 publications

(31 citation statements)

References 21 publications

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“…On one hand, the model-based CF approaches achieved time-aware QoS prediction by formalizing the problem as a user-service-time tensor factorization model [16,30]. On the other hand, the neighborhood-based approaches employ empirical weights to evaluate the joint impacts of historical QoS values over various time intervals for QoS prediction [31,32]. …”

Section: Related Workmentioning

confidence: 99%

Time-Aware Service Ranking Prediction in the Internet of Things Environment

Huang

Cheng

et al. 2017

Sensors

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With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

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Section: Related Workmentioning

confidence: 99%

Time-Aware Service Ranking Prediction in the Internet of Things Environment

Huang

Cheng

et al. 2017

Sensors

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“…Works [26], [27] and [28], which belong to the neighborhood-based CF category, employ empirical weights, e.g., a simple average or an exponentially weighted average, to evaluate the joint impacts of historical QoS information at various time points on QoS prediction. However, it is still hard to capture the temporal dynamics of QoS values precisely only with those empirical techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Q2: Does the proposed personalized QoS prediction approach, which integrates time series forecasting with CF, outperform the traditional neighborhood-based CF algorithm [18], [19] and our previous Time Aware and Data Sparsity Tolerant CF approach (TADST) [27], [28]?…”

Section: Study Setupmentioning

confidence: 99%

“…The value of TopK varies from 2 to 20, with a step value of 2. Finally, we respectively apply the proposed personalized QoS prediction approach, the Time Aware and Data Sparsity Tolerant web service recommendation method (TADST) proposed in our previous works [27], [28] and the traditional neighborhood-based hybrid CF algorithm named WSRec [18], [19], which has been accepted as a de facto benchmark by the community and used in several other studies as well, to the training and test matrices to compare their prediction accuracy.…”

Section: Study Setupmentioning

confidence: 99%

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Personalized Qos Prediction Using Time Series Forecasting and Collaborative Filtering

Hu¹,

Peng²,

Hu³

et al. 2016

STSC

Self Cite

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Quality of Service (QoS) has been widely used for web service recommendation and selection. Since QoS information usually cannot be predetermined, how to make personalized QoS prediction precisely becomes a big challenge. Time series forecasting and Collaborative Filtering (CF) are two mainstream technologies for QoS prediction. However, existing time series forecasting approaches based on AutoRegressive Integrated Moving Average (ARIMA) models do not take new observations as a feedback to revise QoS forecasts. Moreover, they only focus on forecasting QoS values for each individual web service. Service users' personalized factors are not considered. CF facilitates personalized QoS evaluation, but rarely models the temporal dynamics of QoS values. To address the limitations of existing approaches, this paper proposes a novel personalized QoS prediction approach considering both the temporal dynamics of QoS attributes and the personalized factors of service users. Our approach seamlessly combines CF with an improved time series forecasting method, which uses Kalman filtering to compensate for defects of ARIMA models. Additionally, we design a prototype system for QoS dissemination over the Internet, thus providing a necessary infrastructure for the implementation of personalized QoS prediction. Finally, we conduct experiments to study the effectiveness of our approach. Experimental results show our approach can improve QoS prediction accuracy significantly.

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“…The authors in (Hu, Peng & Hu, 2014) proposed a QoS recommendation approach based on Random Walk method. They integrated temporal information into both similarity measurement and QoS prediction of the traditional CF.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Similarity-Based Model for Web Service Recommendation

Abdullah¹,

Li²

2016

STSC

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With the emergence of Cloud and Mobile Computing paradigms which adopt web services (WS) as a means of management, companies worldwide are actively deploying web services within their business environments. Consequently, designing effective Web service recommendation mechanisms is receiving more research attention. Traditional Neighborhood-based Collaborative Filtering (CF) models fail to capture the actual relationships between users and services due to data sparsity. In contrast, Random Walk (RW) algorithm, which is categorized as a sparsitytolerant recommendation approach, suffers from poor recommendation accuracy. In this paper, we first propose an Integrated-Model QoS-based Graph (IMQG), in which users and services represent nodes while weighted Quality of Service (QoS) magnitudes and User/Service similarity measurements serve as edges. Variants of Jaccard coefficient are used to separately compute these similarities. Then, Top-k Random Walk algorithm is applied to generate a final recommendation list. Further, we reduce the model by selecting only a subset of users to better guide RW algorithm. Comprehensive experiments are conducted on a real-world dataset where analysis of results shows high improvement in recommendation accuracy with more tolerance to data sparsity. Utilizing the improved model, a considerable reduction in computation time is achieved while maintaining the recommendation accuracy.

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A Time-Aware and Data Sparsity Tolerant Approach for Web Service Recommendation

Cited by 57 publications

References 21 publications

Time-Aware Service Ranking Prediction in the Internet of Things Environment

Time-Aware Service Ranking Prediction in the Internet of Things Environment

Personalized Qos Prediction Using Time Series Forecasting and Collaborative Filtering

An Efficient Similarity-Based Model for Web Service Recommendation

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