期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2015
卷号：6
期号：1
页码：81-87
语种：English
出版社：Ayushmaan Technologies
摘要：There has been anunprecedented expansion of Internet in last couple of decades; huge amount of information and content isavailable in almost all domains and subjects and is ever expanding in both breadth and depth. On the flip side, this colossal expansion has resulted in data overloading problem; due to which it has become an increasingly difficult task toretrieve useful information from internet and separate out the unwanted ones. Recommender systems have evolved as a solution to the data overload problem that persists today in World Wide Web. Context aware recommender system has been an active research hotspot in current times. It has been found that when contexts parameters are induced appropriately in recommender system, the prediction accuracy increases but if contexts are not properly assimilated, the accuracy of recommender system suffers.The contexts always do not match exactly, but when contexts are meaningfully similar or nearer within a givenknowledge domain, these can be considered and exploited for further processing. This paper discusses the semantic analysis of context attributes of recommender system towards increasing the prediction accuracy and overcome data sparseness.
