In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured data, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions.
Collaboration network of the authors
16 authors ·
27 co-authorship links
· 5 across different organisations
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Pai Chai University
Korea University
Korea Institute of Science & Technology Information
Kyungbok University
Korea Water Resources Corporation (South Korea)
Affiliation unknown
Different organisations
Same organisation
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Thicker line = more joint articles · bigger circle = more articles
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