Ranking Popular Items By Naive Bayes Algorithm

Abstract

The problem of ranking popular items is getting increasing interest from a number of research areas. Several algorithms have been proposed for this task. The described problem of ranking and suggesting items arises in diverse applications include interactive computational system for helping people to leverage social information; in technical these systems are called social navigation systems. These social navigation systems help each individual in their performance and decision making over selecting the items. Based on the each individual response the ranking and suggesting of popular items were done. The individual feedback might be obtained by displaying a set of suggested items, where the selection of items is based on the preference of the individual. The aim is to suggest popular items by rapidly studying the true popularity ranking of items. The difficulty in suggesting the true popular items to the users can give emphasis to reputation for some items but may mutilate the resulting item ranking for other items. So the problem of ranking and suggesting items affected many applications including suggestions and search query suggestions for social tagging systems. In this paper we propose Naïve Bayes algorithm for ranking and suggesting popular items.