News Recommendation in Forum-Based Social Media

Abstract

Self-publication of news on Web sites is becoming a common application platform to enable more engaging interaction among users. Discussion in the form of comments following news postings can be effectively facilitated if the service provider can recommend articles based on not only the original news itself but also the thread of changing comments. This turns the traditional news recommendation to a "discussion moderator" that can intelligently assist online forums. In this work, we present a framework to implement such adaptive news recommendation. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplication, generalization or specialization) between suggested news articles and the original posting is investigated. Experiments indicate that our proposed solutions provide an enhanced news recommendation service in forum-based social media.

Hot News Recommendation System across Heterogonous Websites Using Hadoop

“…Owing largely to the ever-increasing volume and sophistication of information on the web, we are able to access an enormous amount of information from around the globe [1,2,3].Recommender systems are usually classified into three categories, based on how the recommendations are made [4,5]:content-based recommendation ,collaborative filtering and hybrid. Content-based recommender systems: These recommender systems recommend an item to the user similar to the ones the user preferred in the past [6,7]. Collaborative recommender systems: These systems recommend an item to the user based on the people with similar tastes and preferences have liked in the past.…”