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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data Mining Techniques used in the Recommendation of E-commerce services


    Contributors:


    Publication date :

    2018-03-01


    Size :

    7290280 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Design of electronic-commerce recommendation systems based on outlier mining

    Xia, Huosong / Wei, Xiang / An, Wuyue et al. | Online Contents | 2020




    Data Mining Techniques in Bioinformatics

    Zagoruiko, N. G. / Kolchanov, N. A. / Pichueva, A. G. et al. | British Library Online Contents | 2003


    MINING SERVICES

    Online Contents | 2002