期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：11
出版社：S.S. Mishra
摘要：Recommender systems are being extensively used in the present generation. Recommender systems are gradually increasingly harder to find the relevant contents of information in the vast abundant current age of information overload. Thus, recommender systems are needed to help individual users find the most relevant items or products or data sets from an abundant number of choices, collection. Through this gradually increase sales by exposing users to what they might like. E.g. In real time or real world applications consider a product say laptop, the laptop present in numerous patterns with different applications in number depending upon different user's requirements. Thus providing a user or the customer with relevant information about the product as per their requirements with the help of recommender systems would ease the work of an user. Hence we can conclude saying that the volume of information available in the current age is huge to individual users (for e.g., e-commerce sites applications such as Amazon, Netflix) and hence focusing in developing some recommendation techniques within both industry and academia. Most, research to date is focusing on improving the recommendation accuracy i.e. the accuracy with which the recommender system predicts users ratings for items that are yet to be rated. The diversity of recommendation also plays an important role to be considered, it is important to explore the relationship between the accuracy and diversity and also the recommendation quality. Empirical analysis consistently shows the diversity gains of different recommendation techniques which is being used in several real world rating applications or datasets and uses different rating prediction algorithms. Individual users and online content providers will also benefit from the proposed approaches, where in which each user can find more relevant and personalized items or products from accurate and diverse recommendations provided by these recommender systems. These approaches, ranking techniques and algorithms could potentially lead to increased loyalty and sales in e-commerce application sites, thus benefiting the providers as well.
