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Nowadays, the internet is playing a major role in everyday life, becoming the first choice for sharing information. In the last sixteen years the number of websites increased from three million to almost one billion. An important contribution to this number was brought by the content management systems which provided an easy approach for any user to create its own website without having advanced programming...
The increasing volume of spam has become a serious threat not only to the Internet, but also to the society. However, it's a great challenge to discover the spam from the Internet effectively and efficiently. Content-based filtering is one of the mainstream methods to solve the problem. This paper proposed a content based spam topic detection strategy through keyword extraction. In particular, spam...
Web based commercial recommender systems (RS) can help users to make decisions about which product to purchase from the vast amount of products available on the Internet. Currently, many commercial recommender systems are developed for recommending frequently purchased products where a large amount of explicit ratings or purchase history data is available to predict user preferences. However, for...
The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free texts or legal documents. However, the problem is that sources of domain knowledge are time consuming to build and equally costly to maintain. As a...
With the rapid growth of e-commerce, there has been millions of products in a large ecommerce site where customer unable to effectively choose the products they are exposed to. To overcome the product overload problem, a variety of recommendation methods have been developed. Collaborative filtering (CF) is the most successful recommendation method. However, the CF method has two well-known limitations,...
A majority of web personalization research concentrates on customizing a single website. On the contrary, recommending web pages across websites is the focus of this study. We emphasize that eliciting user interests among different topics within a domain is an important concern in cross-website page recommendations. Enhancing Wikipedia's categorization system through heuristic information extraction,...
Web based applications are increasing at an enormous speed and consequently its users are also increasing at an exponential speed. The evolutionary changes in technology have made it possible to capture the users' essence and interactions with web applications through web server log file as web usage. The web usage Mining (WUM) is the process of discovering hidden patterns from the web usage. Due...
For blocking pornographic, illegal websites by Intenet host domain, now the majority of solutions are based on the identification and blocking by software. Basing on the analysis of Bloom Filter algorithm and combining with the feature of FPGA chip, this paper proposes an efficient, high-speed hardware-based Implementation of the host domain blocking.
Dakota State University has a unique computing environment. Every undergraduate student and professor has a school assigned tablet PC. In addition, it is the State of South Dakota's premier four year technology university. The university also hosts email, website, and website services for the state's K-12 program in addition to its own. The high use of the mobile computing environment serves as a...
Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The performance of this kind of system depends on a large amount of information. At the same time, the amount of information on the Web is continuously growing, especially due to increased User Generated Content since the apparition...
Internet is a huge source of information. Search engines have indexed much of this information and are able to extract the relevant webpages that are related to a given query. However, once the search engine retrieves a set of webpages, the user has to read the webpages in order to find the relevant information. This is a time consuming task because webpages often mix information related to different...
In this paper we compare four machine learning techniques for blog comments spam filtering. the machine learning techniques are the Naïve Bayes, K-nearest neighbor, neural networks and the support vector machines. For this comparative study we used a blog comment corpus that has been affected by spam, which is our study case in this work. We classify the comments of this blog comments corpus, which...
In the past decade the massive growth of the Internet brought huge changes in the way humans live their daily life; however, the biggest concern with rapid growth of digital information is how to efficiently manage and filter unwanted data. In this paper, we propose a method for managing RSS feeds from various news websites. A Web service was developed to provide filtered news items extracted from...
One-Class Collaborative Filtering (OCCF) problems are more problematic than traditional collaborative filtering problems, since OCCF datasets lack counter-examples. Social networks can be used to remedy dataset issues faced by OCCF applications. In this work, we compare social networks belong to specific domains and the ones belong to more generic domains in terms of their usability in OCCF problems...
Social media, e.g. Weblog and Internet forum, generate rich historical textual datasets which record lots of valuable events. Automatic event detection tries to discover important and interesting events and their related documents. Existing solutions to event detection, however, are mostly proposed for high quality news stories and may not work well when they are applied to noisy social media datasets,...
We present a new interface for exploring and navigating large-scale discussions on the internet. Our system, tldr, focuses on three primary user goals: finding, navigating and filtering content based on the users' own relevance criteria. We employ a progressive design approach that combines user research from the popular web site Reddit.com and visualizations from existing discussion data. Based on...
Nowadays, the emphasis on Web 2.0 is specially focused on user generated content, data sharing and collaboration activities. Protocols like RSS (Really Simple Syndication) allow users to get structured web information in a simple way, display changes in summary form and stay updated about news headlines of interest. In the e-Learning domain, RSS feeds meet demand for didactic activities from learners...
In recent years, the blog has become the most typical social media for citizens to share their opinions. In addition, a large number of blogs reflect current social trends or major issues. Especially, more than thousand articles and more than 10,000 responding messages (comments) are registered on a well-known blog in a day. It is hard to search and explore useful messages on blogs since most blog...
With more and more reviews on the Web, browsing through a mass of the related reviews becomes a heavy work. How to effectively analyzing and organizing these reviews attracts more attention. This paper pursues on the analysis of product reviews. It focuses on the product features that customer commented on and also whether their opinions are positive or negative. Different from the traditional method,...
In social news services, selecting valuable and credible news content is one of the most important issues. In traditional journalism, a small number of people called editors selected news that they considered worthwhile. Recently, several services have utilized reader voting to find news that is popular or credible but most of them are prone to abuse. In this paper, we present a collaborative news...
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