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온라인 구매 행태를 고려한 토픽 모델링 기반 도서 추천

Topic Modeling-based Book Recommendations Considering Online Purchase Behavior

조윤호|정영진
한국지식경영학회 (The Knowledge Management Society of Korea)|2017.12|지식경영연구 (Knowledge Management Research)|제18권 제4호|pp. 97-118|DOI 10.15813/kmr.2017.18.4.004

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키워드

Topic ModelingText MiningCollaborative FilteringBig data AnalysisBook Recommender System
주제분류경영학

초록

Thanks to the development of social media, general users become information and knowledge providers. But customers also feel difficulty to decide their purchases due to numerous information. Although recommender systems are trying to solve these information/knowledge overload problem, it may be asked whether they can honestly reflect customers’ preferences. Especially, customers in book market consider contents of a book, recency, and price when they make a purchase. Therefore, in this study, we propose a methodology which can reflect these characteristics based on topic modeling and provide proper recommendations to customers in book market. Through experiments, our methodology shows higher performance than traditional collaborative filtering systems. Therefore, we expect that our book recommender system contributes the development of recommender systems studies and positively affect the customer satisfaction and management.

Abstract

Thanks to the development of social media, general users become information and knowledge providers. But customers also feel difficulty to decide their purchases due to numerous information. Although recommender systems are trying to solve these information/knowledge overload problem, it may be asked whether they can honestly reflect customers’ preferences. Especially, customers in book market consider contents of a book, recency, and price when they make a purchase. Therefore, in this study, we propose a methodology which can reflect these characteristics based on topic modeling and provide proper recommendations to customers in book market. Through experiments, our methodology shows higher performance than traditional collaborative filtering systems. Therefore, we expect that our book recommender system contributes the development of recommender systems studies and positively affect the customer satisfaction and management.

참고문헌 (48)

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