An Adaptive Social Media Recommendation System

People now-a-days read news online which has become very popular as the web provides access to news articles from millions of sources around the world. News Recommendation is to select relevant news by their themes. Identifying news based on the topic is critical in this task. News is proposed solely based on the author’s point of view. In this system we immensely enhance the performance of recommendation. This can be achieved if the user interaction is better utilized. It overcomes the bias of traditional news proposal by suggesting relevant information with a balanced perspective of authors and readers. This is achieved by identifying and using the topic patterns of the original news posting and its comments, one of the most useful records of user behaviors in social media. In particular, to capture the dynamic concerns of users, hidden topic patterns are extracted by utilizing both textual and structural information of comments. Certain keywords from comment is extracted and compared with the keywords which are already stored in database. To do so, we model the relationship among comments and that relative to the original posting. Our proposed solution provides an effective news recommendation service. As this system takes suggestion of both author and reader, system will help to provide effective news recommendation service. This system uses unique algorithm to develop news recommendation service which is different from the traditional news proposal. This system will help web users to access relevant news articles by their themes.