Related papers: Fuzzy Information Entropy and Region Biased Matrix…

Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering is currently most widely used approach to build Recommendation System. CF techniques uses the…

Building high quality cloud applications becomes an urgently required research problem. Nonfunctional performance of cloud services is usually described by quality-of-service (QoS). In cloud applications, cloud services are invoked remotely…

In recent years, the number of online services has grown rapidly, invoke the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of…

In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those methods include the combinations of three ideal vector…

The large scale content distribution systems were improved broadly using the replication techniques. The demanded contents can be brought closer to the clients by multiplying the source of information geographically, which in turn reduce…

We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization. As a general…

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here…

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict…

Service composition is a widely used method in ubiquitous computing that enables accomplishing complex tasks required by users based on elementary (hardware and software) services available in ubiquitous environments. To ensure that users…

Dead Reckoning mechanisms are usually used to estimate the position of simulated entity in virtual environment. However, this technique often ignores available contextual information that may be influential to the state of an entity,…

Matrix factorization (MS) is a collaborative filtering (CF) based approach, which is widely used for recommendation systems (RS). In this research work, we deal with the content recommendation problem for users in a content management…

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous…

Cloud services today are increasingly built using functionality from other running services. In this paper, we question whether legacy Quality of Services (QoS) metrics and enforcement techniques are sufficient as they are producer centric.…

Matrix factorization (MF) is a classical collaborative filtering algorithm for recommender systems. It decomposes the user-item interaction matrix into a product of low-dimensional user representation matrix and item representation matrix.…

Although Recommender Systems have been comprehensively studied in the past decade both in industry and academia, most of current recommender systems suffer from the following issues: 1) The data sparsity of the user-item matrix seriously…

Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have…

Today, numerous web services with similar functionalities are available on the Internet. Users often evaluate the Quality of Service (QoS) to choose the best option among them. Predicting the QoS values of these web services is a…

We describe methods to predict a crowd worker's accuracy on new tasks based on his accuracy on past tasks. Such prediction provides a foundation for identifying the best workers to route work to in order to maximize accuracy on the new…

The prediction of residential power usage is essential in assisting a smart grid to manage and preserve energy to ensure efficient use. An accurate energy forecasting at the customer level will reflect directly into efficiency improvements…

Matrix factorization (MF) has been widely used to discover the low-rank structure and to predict the missing entries of data matrix. In many real-world learning systems, the data matrix can be very high-dimensional but sparse. This poses an…