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A Topic Modeling-based Recommender System Considering Changes in User Preference
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So Young Kang (Department of Business Administration, Graduate School, Kyung Hee Univesity)
Jae Kyeong Kim (School of Management Kyung Hee University)
Il Young Choi (School of Business Administration Kyung Hee Univesity)
Chang Dong Kang (Department of Business Administration, Graduate School, Kyung Hee Univesity)
Vol. 26, No. 2, Page: 43 ~ 56
Keywords
Recommender system, Topic modeling, Sequential association rule mining, Sequence analysis, Topic pattern, Explainability
Abstract
Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference.
Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to “comedy shows”, “high-teen drama series”, “crime investigation drama”, “horror theme”, “British drama”, “medical drama”, “science fiction drama”, respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations
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고객 선호 변화를 고려한 토픽 모델링 기반 추천 시스템
강소영 (경희대학교)
김재경 (경희대학교)
최일영 (경희대학교)
강창동 (경희대학교)
Keywords
추천 시스템, 토픽 모델링, 순차 연관 규칙, 순차 분석, 토픽 패턴, 설명가능성
Abstract
추천 시스템은 사용자가 다양한 옵션 중에서 최선의 선택을 할 수 있도록 도와준다. 그러나 추천시스템이 상업적으로 성공하기 위해서는 극복할 몇 개의 문제점이 존재한다. 첫째, 추천시스템의 투명성 부족 문제이다. 즉, 추천된 상품이 왜 추천되었는지 사용자들이 알 수 없다. 둘째, 추천시스템이 사용자 선호의 변화를 즉각적으로 반영할 수 없는 문제이다. 즉, 사용자의 상품에 대한 선호는 시간이지남에 따라 변함에도 불구하고, 추천시스템이 사용자 선호를 반영하기 위해서는 다시 모델을 재구축해야 한다. 따라서 본연구에서는 이러한 문제를 해결하기 위해 토픽 모델링과 순차 연관 규칙을 이용한 추천 방법론을 제안하였다. 토픽 모델링은 사용자에게 아이템이 왜 추천되었는지 설명하는데 유용하며, 순차 연관 규칙은 변화하는 사용자의 선호를 파악하는데 유용하다. 본 연구에서 제안한 방법은크게 토픽 모델링 및 사용자 프로파일 생성 등 토픽 모델링에 기반한 사용자 프로파일 생성 단계와토픽에 사용자 선호 확인 및 순차 연관 규칙 발견 등 순차 연관 규칙에 기반한 추천 단계로 구분된다. 벤치마크 시스템으로 협업 필터링 기반 추천 시스템을 개발하고, 아마존의 리뷰 데이터 셋을 이용하여제안한 방법론의 성능을 비교 평가하였다. 비교 분석 결과, 제안한 방법론이 협업 필터링 기반 추천시스템보다 뛰어난 성능을 보였다. 따라서 본 연구에서 제안하는 추천 방법을 통해 추천 시스템의 투명성을 확보할 수 있을 뿐만 아니라, 시간에 따라 변화하는 사용자의 선호를 반영할 수 있다. 그러나 본연구는 토픽과 관련된 상품을 추천하기 때문에, 토픽에 포함된 상품의 수가 많을 경우 추천이 정교하지 못하는 한계점이 있다. 또한 토픽의 수가 적기 때문에 토픽에 대한 순차 연관 규칙이 너무 적은문제점이 있다. 향후 연구에서 이러한 문제점을 해결한다면 좋은 연구가 될 것으로 판단된다.
Cite this article
JIIS Style
Kang, S. Y., J. K. Kim, I. Y. Choi, and C. D. Kang, "A Topic Modeling-based Recommender System Considering Changes in User Preference", Journal of Intelligence and Information Systems, Vol. 26, No. 2 (2020), 43~56.

IEEE Style
So Young Kang, Jae Kyeong Kim, Il Young Choi, and Chang Dong Kang, "A Topic Modeling-based Recommender System Considering Changes in User Preference", Journal of Intelligence and Information Systems, vol. 26, no. 2, pp. 43~56, 2020.

ACM Style
Kang, S. Y., Kim, J. K., Choi, I. Y., and Kang, C. D., 2020. A Topic Modeling-based Recommender System Considering Changes in User Preference. Journal of Intelligence and Information Systems. 26, 2, 43--56.
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@article{Kang:JIIS:2020:809,
author = {Kang, So Young and Kim, Jae Kyeong and Choi, Il Young and Kang, Chang Dong},
title = {A Topic Modeling-based Recommender System Considering Changes in User Preference},
journal = {Journal of Intelligence and Information Systems},
issue_date = {June 2020},
volume = {26},
number = {2},
month = Jun,
year = {2020},
issn = {2288-4866},
pages = {43--56},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Recommender system, Topic modeling, Sequential association rule mining, Sequence analysis, Topic pattern and Explainability
},
}
%0 Journal Article
%1 809
%A So Young Kang
%A Jae Kyeong Kim
%A Il Young Choi
%A Chang Dong Kang
%T A Topic Modeling-based Recommender System Considering Changes in User Preference
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 26
%N 2
%P 43-56
%D 2020
%R
%I Korea Intelligent Information System Society