作者: Rishabh Malhotra , Anu Taneja , Anuja Arora
关键词:
摘要: With the advancements in technology, a rich amount of research content is available on web that creates situation dilemma for researchers to choose most appropriate and trending topic further has wide scope future too. The prediction authors' interests always been challenging task as might change with time. This demands need an efficient temporal recommender system takes into account drifting users study addresses this major challenge analyses based similar their own long-term short-term interests. proposed model extends latent Dirichlet allocation (LDA) capture aspects utilizes semantic analysis (LSA) method along word-net feature incorporate understanding topics implemented real NIPS dataset collection 2000 conference papers. empirical validates achieves accuracy about 62% predictions due fusion