Publication

Abstract : Recommender systems and web search engines have gained a lot of importance in today's digital platform. In today's digital world everything (from buying to selling) has come to internet platform. Due to huge amount of data large scale processing is required. Today large amount of data is obtained from e-commerce services, application data, web data etc. This large-scale data processing involves many similarity search algorithms for giving recommendations. Many e-commerce services and applications use similarity search for giving valuable suggestions and showing the related documents. In this paper, we discuss the similarity search algorithms, PathSim and SimRank. We compare and contrast both the algorithms by taking different datasets. We suggest that the efficiency of the website improves if the algorithms are used in respective scenarios. The time complexities of both the algorithms are compared to check.