Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering

Hu, Yan; Peng, Qimin; Hu, Xiaohui; Yang, Rong

doi:10.1109/tsc.2014.2381611

Cited by 100 publications

(52 citation statements)

References 29 publications

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“…Existing approaches such as content‐based, memory‐based, and trust‐based are generally continuous. Shifting of service bias and shifting of user preferences are also the most used time effects in categorical TARS.…”

Section: Time Information In Service Recommendationmentioning

confidence: 99%

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Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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Summary With the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.

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Section: Time Information In Service Recommendationmentioning

confidence: 99%

“…Memory‐based CF is used to exploit full data or a sample of it in order to generate a suitable forecast . This sub‐class is mainly characterized by its high effectiveness, in addition to its simplicity of implementation .…”

Section: Related Workmentioning

confidence: 99%

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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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

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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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A topic community‐based method for friend recommendation in large‐scale online social networks

Fei

et al. 2016

Concurrency and Computation

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Online social networks (OSNs) have become more and more popular and have attracted a great many users. Friend recommendation, which is one of the important services in OSN, can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider either user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via Nonnegative Matrix Factorization (NMF). This method first applies joint NMF model to mine topic communities existing in OSN by combing link and content information. Then it computes user pairwise similarities and makes friends recommendation based on topic communities. Furthermore, this method can be implemented using the MapReduce distributed computing framework. Extensive experiments show that our proposed method not only has better recommendation performance than state-of-the-art methods but also has good scalability to deal with the problem of friend recommendation in large-sale OSNs. Moreover, the application case demonstrates that it can significantly improve friend recommendation service in the real world OSN.

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Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering

Cited by 100 publications

References 29 publications

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Personalized Qos Prediction Using Time Series Forecasting and Collaborative Filtering

A topic community‐based method for friend recommendation in large‐scale online social networks

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