Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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.
How to cite this article:
Young Jun Kim, Jeong Min Park, Sung Taek Chung and Jeong Joon Kim. Keyword-Based Collaborative Filter Recommendation System Using Scraping.
DOI: https://doi.org/10.36478/jeasci.2018.1506.1514
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2018.1506.1514