@article{MAKHILLJEAS201813615812,
    title = {Keyword-Based Collaborative Filter Recommendation System Using Scraping},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {6},
    pages = {1506-1514},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.1506.1514},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.1506.1514},
    author = {Jeong,Young,Jeong and},
    keywords = {Collaboration filter,recommendation system,scraping,movie recommend,keyword-based,purchase},
    abstract = {CF (Collaborative Filtering) is one of the methods generally utilized in recommendation system. The
goal of CF is to analyze the purchase trend of other customers similar to a target customer and recommend items
that can be preferred by the customer among the items he or she has not bought. Conventional CF, however
is hardly capable of predicting any new customer’s purchase trend for they have no existing purchase list. To
resolve the problem, it surveys customers too much or changes items into profile, causing huge expenses and
difficulty. In this study, as a new method to solve such a problem, keyword-based collaborative filter
recommendation system using scraping is proposed.}
    }