期刊名称：International Journal of Electronics Communication and Computer Engineering
印刷版ISSN：2249-071X
电子版ISSN：2278-4209
出版年度：2015
卷号：6
期号：3
页码：435-440
出版社：IJECCE
摘要：Recommender systems use historical data on user preferences and other available data on users and items to predict items a new user might like. Applications of these methods include recommending items for purchase and personalizing the browsing experience on a web-site. Collaborative filtering methods have focused on using just the history of user preferences to make the recommendations. These methods have been categorized as memory-based if they operate over the entire data to make predictions and as model-based if they use the data to build a model which is then used for predictions. Among various recommendation techniques, neighborhood- based Collaborative Filtering (CF) techniques have been one of the most widely used and best performing techniques in literature and industry. This paper proposes new approaches that can enhance the neighborhood-based CF techniques by identifying a few best neighbors (the most similar users to a target user) more accurately with more information about neighbors. To aid in the decision-making process, recommender systems use the available data on the items themselves. Personalized recommender systems subsequently use this input data, and convert it to an output in the form of ordered lists or scores of items in which a user might be interested. These lists or scores are the final result the user will be presented with, and their goal is to assist the user in the decision-making process. The application of recommender systems outlined was just a small introduction to the possibilities of the extension. Recommender systems became essential in an information- and decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time
