Content Modelling for unbiased Information Analysis

Content is the form through which the information is conveyed as per the requirement of user. A volume of content is huge and expected to grow exponentially hence classification of useful data and not useful data is a very tedious task. Interface between content and user is Search engine. Therefore, the contents are designed considering search engine\u27s perspective. Content designed by the organization, utilizes user’s data for promoting their products and services. This is done mostly using inorganic ways utilized to influence the quality measures of a content, this may mislead the information. There is no correct mechanism available to analyse and disseminate the data. The gap between Actual results displayed to the user and results expected by the user can be minimized by introducing the quality check for the parameter to assess the quality of content. This may help to ensure the quality of content and popularity will not be allowed to precede quality of content. Social networking sites will help in doing the user modelling so that the qualitative dissemination of content can be validated

Content Modelling for unbiased Information Analysis

Content is the form through which the information is conveyed as per the requirement of user. A volume of content is huge and expected to grow exponentially hence classification of useful data and not useful data is a very tedious task. Interface between content and user is Search engine. Therefore, the contents are designed considering search engine\u27s perspective. Content designed by the organization, utilizes user’s data for promoting their products and services. This is done mostly using inorganic ways utilized to influence the quality measures of a content, this may mislead the information. There is no correct mechanism available to analyse and disseminate the data. The gap between Actual results displayed to the user and results expected by the user can be minimized by introducing the quality check for the parameter to assess the quality of content. This may help to ensure the quality of content and popularity will not be allowed to precede quality of content. Social networking sites will help in doing the user modelling so that the qualitative dissemination of content can be validated

Online Social Voting Techniques in Social Networks Used for Distinctive Feedback in Recommendation Systems

Internet voting is the process of collection of opinions on a particular, characterized issue to collect data about items like individuals, items, and administrations et cetera. A voting method can be utilized as a rating process by adding another measurement to it as far as the gathering meaning of ratable articles. Social networks like Twitter, LinkedIn, Facebook, and Google+ have increased noteworthy consideration as of late. Individuals began depending more on a social network for complex data prerequisites. Voting help applications are fundamentally used to prompt voters in choosing the correct option. Vote recommendation frameworks typically abused amid decisions, might be reached out to the choice of appropriate items and administrations in light of user inclinations, ratings, reviews, and profiles. Suggested System misuses relationship among users by the method for item recommendation. Mining the productive reviews from the user comments, votes, and inclinations is an intriguing territory of research as of late. The advanced patterns of information and the materialness of the recommendation procedures to fulfill the present data needs is pointed. The extensibility of the voting prompting systems/recommendation strategies in different settings is talked about alongside the proposition for new methodology that suit the present data needs

Personalized Recommendation Systems (PRES): A Comprehensive Study and Research Issues.

The type of information systems used to recommend items to the users are called Recommendation systems. The concept of recommendations was seen among cavemen, ants and other creatures too. Users often rely on opinion of their peers when looking for selecting something, this usual behavior of the humans, led to the development of recommendation systems. There exist various recommender systems for various areas. The existing recommendation systems use different approaches. The applications of recommendation systems are increasing with increased use of web based search for users’ specific requirements. Recommendation techniques are employed by general purpose websites such as google and yahoo based on browsing history and other information like user’s geographical locations, interests, behavior in the web, history of purchase and the way they entered the website. Document recommendation systems recommend documents depending on the similar search done previously by other users. Clickstream data which provides information like user behavior and the path the users take are captured and given as input to document recommendation system. Movie recommendation systems and music recommendation systems are other areas in use and being researched to improve. Social recommendation is gaining the momentum because of huge volume of data generated and diverse requirements of the users. Current web usage trends are forcing companies to continuously research for best ways to provide the users with the suitable information as per the need depending on the search and preferences. This paper