Journal ArticleDOI
Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation
TLDR
The experimental results indicate that the proposed location-aware personalized CF method improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.Abstract:
Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.read more
Citations
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Journal ArticleDOI
Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios
TL;DR: A novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-me means) called TCCF is proposed, which can provide a higher quality recommendation by analyzing the user's behaviors and cluster similar users together for further quick and accurate recommendation.
Journal ArticleDOI
Location-Aware Deep Collaborative Filtering for Service Recommendation
TL;DR: A new deep CF model for service recommendation, named location-aware deep CF (LDCF), which can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem.
Journal ArticleDOI
Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization
TL;DR: CNMF is proposed, a covering-based quality prediction method for Web services via neighborhood-aware matrix factorization that significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.
Journal ArticleDOI
A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications
TL;DR: A framework of the CF recommender system based on various user data including user ratings and user behaviors is proposed, and several typical CF algorithms are classified as memory-based approaches and model- based approaches and compared.
Journal ArticleDOI
Deep hybrid collaborative filtering for Web service recommendation
TL;DR: A novel deep learning based hybrid approach for Web service recommendation by combining collaborative filtering and textual content is proposed, which can achieve better recommendation performance than several state-of-the-art methods.
References
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Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Journal Article
Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.