k - Nearest Neighbor Rule is a well-known technique for text classification. The reason behind this is its simplicity, effectiveness, easily modifiable. In this paper, we briefly discuss text classification, k-NN algorithm and analyse the sensitivity problem of k value. To overcome this problem, we introduced inverse cosine distance weighted voting function for text classification. Therefore, Accuracy of text classification is increased even if any large value for k is chosen, as compared to simple k Nearest Neighbor classifier. The proposed weighted function is proved as more effective when any application has large text dataset with some dominating categories, using experimental results.
Tremendous growth of online music data has given new opportunities for building more effective music recommender systems. These systems help users to find and categorize songs according to their likings. The main goal of Recommender Systems (RS) is to predict ratings of items that the users would be interested in. With the rapid development of the Collaborative Tagging approach, tags could be fascinating and helpful information to enrich RS systems. Attributes are the "global" depictions of items while tags are "local" depictions of items provided by the users.Explicit feedback and implicit feedback demonstrates distinct properties of users' preferences with both advantages and disadvantages. Combination of these in a user preference model not only exhibits a number of disputes but can also overwhelm the problems related with each other. Hence, to take advantage of tagging data and see whether better recommendations are generated or not, a novel method for music recommendation is proposed that combines implicit feedback and explicit feedback of the users. Also, both explicit types of feedbacks are normalized before transformation into ratings in order to provide the desired ratings in case of skewed play counts data.
Web 2.0 applications have become ubiquitous over the past few years because they provide useful features such as a rich, responsive graphical user interface that supports interactive and dynamic content. Social networking websites, blogs, auctions, online banking, online shopping and video sharing websites are noteworthy examples of Web 2.0 applications. The market for public cloud service providers is growing rapidly, and cloud providers offer an ever-growing list of services. As a result, developers and researchers find it challenging when deciding which public cloud service to use for deploying, experimenting or testing Web 2.0 applications. This study compares the scalability and performance of a social-events calendar application on two Infrastructure as a Service (IaaS) cloud services – Amazon EC2 and HP Cloud. This study captures and compares metrics on three different instance configurations for each cloud service such as the number of concurrent users (load), as well as response time and throughput (performance). Additionally, the total price of the three different instance configurations for each cloud service is calculated and compared. This comparison of the scalability, performance and price metrics provides developers and researchers with an insight into the scalability and performance characteristics of the three instance configurations for each cloud service, which simplifies the process of determining which cloud service and instance configuration to use for deploying their Web 2.0 applications. This study uses CloudStone – an open-source, three-tier web application benchmarking tool that simulates Web 2.0 application activities – as a realistic workload generator and to capture the intended metrics. The comparison of the collected metrics indicates that all of the tested Amazon EC2 instance configurations provide better scalability and lower latency at a lower cost than the respective HP Cloud instance configurations; however, the tested HP Cloud instance configurations provide a greater storage capacity than the Amazon EC2 instance configurations, which is an important consideration for data-intensive Web 2.0 applications.
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