Journal ArticleDOI
Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation
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TLDR
A cube model is designed to explicitly describe the relationship among providers, consumers and Web services, and a Standard Deviation based Hybrid Collaborative Filtering (SD-HCF) for Web Service Recommendation (WSRec) and an Inverse consumer Frequency based User Collaborativefiltering (IF-UCF), which indicates the effectiveness of adding inverse consumer frequency to UCF.Abstract:
Web service recommendation has become a hot yet fundamental research topic in service computing. The most popular technique is the Collaborative Filtering (CF) based on a user-item matrix. However, it cannot well capture the relationship between Web services and providers. To address this issue, we first design a cube model to explicitly describe the relationship among providers, consumers and Web services. And then, we present a Standard Deviation based Hybrid Collaborative Filtering (SD-HCF) for Web Service Recommendation (WSRec) and an Inverse consumer Frequency based User Collaborative Filtering (IF-UCF) for Potential Consumers Recommendation (PCRec). Finally, the decision-making process of bidirectional recommendation is provided for both providers and consumers. Sets of experiments are conducted on real-world data provided by Planet-Lab. In the experiment phase, we show how the parameters of SD-HCF impact on the prediction quality as well as demonstrate that the SD-HCF is much better than extant methods on recommendation quality, including the CF based on user, the CF based on item and general HCF. Experimental comparison between IF-UCF and UCF indicates the effectiveness of adding inverse consumer frequency to UCF.read more
Citations
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Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
TL;DR: Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.
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Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors
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A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease
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Unified Collaborative and Content-Based Web Service Recommendation
TL;DR: This paper proposes a novel approach that unifies collaborative filtering and content-based recommendation of web services using a probabilistic generative model, which outperforms the state-of-the-art methods on recommendation performance.
References
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Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Book
Nonparametric Statistical Methods
Myles Hollander,Douglas A. Wolfe +1 more
TL;DR: An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
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.
Journal ArticleDOI
Amazon.com recommendations: item-to-item collaborative filtering
TL;DR: Item-to-item collaborative filtering (ITF) as mentioned in this paper is a popular recommendation algorithm for e-commerce Web sites that scales independently of the number of customers and number of items in the product catalog.