• Title/Summary/Keyword: 추천행동

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A Schedule Recommendation Agent using Hierarchical Behavior Selection Network (계층적 행동선택 네트워크를 이용한 일정추천 에이전트)

  • Yang, Kyon-Mo;Cho, Sung-Bae
    • Proceedings of the Korean Information Science Society Conference
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    • 2012.06b
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    • pp.390-392
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    • 2012
  • 인공비서 에이전트는 일정관리 및 추천을 할 때 일정이 비어 있는 시간을 계산하는 비교적 간단한 방식을 사용하는데, 보다 유용한 추천을 위해서는 사용자의 상황과 일정의 수행 조건을 고려하여야 한다. 본 논문에서는 환경의 변화에 유연하게 대응할 수 있는 행동선택 네트워크를 사용하는 일정추천 에이전트를 개발한다. Maes가 제안한 행동선택 네트워크를 현실적인 문제에 적용하는 데는 목적과 행동 노드의 개수가 크게 늘어나면 문제가 있다. 이를 해결하기 위해 행동선택 네트워크를 모듈화 하여 목적간의 충돌을 방지하고, 모듈화를 할 때 선행 행동 연결을 통한 모듈간의 사라진 연결을 보완하며, 목적들 간의 연관관계를 표현하기 위한 상위 행동선택 네트워크를 두는 계층적 행동선택 네트워크 방식을 제안한다. 제안하는 방법을 사용하여 몇가지 시나리오에 따른 일정추천 실험을 통하여 제안한 에이전트의 유용성을 확인하였다.

Deep Learning-Based Personalized Recommendation Using Customer Behavior and Purchase History in E-Commerce (전자상거래에서 고객 행동 정보와 구매 기록을 활용한 딥러닝 기반 개인화 추천 시스템)

  • Hong, Da Young;Kim, Ga Yeong;Kim, Hyon Hee
    • KIPS Transactions on Software and Data Engineering
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    • v.11 no.6
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    • pp.237-244
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    • 2022
  • In this paper, we present VAE-based recommendation using online behavior log and purchase history to overcome data sparsity and cold start. To generate a variable for customers' purchase history, embedding and dimensionality reduction are applied to the customers' purchase history. Also, Variational Autoencoders are applied to online behavior and purchase history. A total number of 12 variables are used, and nDCG is chosen for performance evaluation. Our experimental results showed that the proposed VAE-based recommendation outperforms SVD-based recommendation. Also, the generated purchase history variable improves the recommendation performance.

Predicting personal activity categories for POI recommendation (방문지 추천을 위한 개인 행동 범주 예측)

  • Byeong-Il Hwang;Dong-Ju Kim
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.07a
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    • pp.5-6
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    • 2023
  • 본 연구에서는 언텍트 소비가 일반화됨에 따라 소상공인들을 지원하기 위해 캡티브-포털을 활용하여 주문하는 등의 시스템을 구축하고 있으며, 이에 상권 내 방문자들의 주문 정보를 기반으로 개인의 선호나 취향을 고려하고 기존 방문 순서를 고려하여 다음 방문지를 추천할 수 있는 모델을 개발하고자 한다. 모델 개발을 위한 데이터셋으로는 캡티브-포털을 통해 수집되는 변수 항목과 유사한 위치기반 SNS 데이터인 Foursquare 데이터를 활용했다. 본 논문에서는 데이터셋의 변수 중 상호명을 기반으로 22개의 행동 유형 카테고리로 묶어 현재 행동 유형 이후에 다음에 이어질 행동 유형을 예측하는 것을 제안한다. 개인 별 세션 기반의 데이터셋을 LightMove 알고리즘을 활용하여 행동유형 예측을 임베딩 차원의 변경하여 실험한 결과 500차원에서 Top-5가 82.72의 성능을 보임을 확인했다. 향후 국내 상권에 맞는 방문지 추천 시스템이 개발된다면 방문지 추천을 활용하여 다양한 마케팅 전략을 수립이 가능해질 수 있고, 이를 통해 지역 상권이 활성화될 것으로 기대된다.

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A Study on User behavior-based multi-attribute attitude models and based on cross-correlation (사용자 행동 기반 다속성 태도 모델 기반의 유사도 측정 연구)

  • Ahn, Byung-IK;Jung, Ku-Imm;Choi, Hae-Lim
    • Proceedings of the Korea Information Processing Society Conference
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    • 2016.04a
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    • pp.554-557
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    • 2016
  • 2015년 우리나라 스마트폰 보급률이 83%에 다다르고 인터넷 정보 검색은 PC보다 모바일이 추월한지 오래다. 범람하는 정보 안에서 편하고 빠른 것에 익숙해진 사용자들은 이제 개인화된 맞춤형 추천 정보의 제공을 원한다. 맞춤형 추천을 위해서는 사용자의 행동을 이해하고 추천하는 것이 필요하다. 현재 대중화된 개인 추천 서비스는 책과 영화가 있는데 생활에 많은 부분을 차지하고 있는 음식점 방문에 대해서도 맞춤형 추천 서비스를 제공해 줄 수 있다. 본 논문에서는 음식점 방문에 대한 비슷한 태도를 보인 사용자를 추출한 후 방문했던 장소를 비교하여 추천하는 사용자 행동 기반 다속성 태도 모델 기반의 장소 추천 모델을 연구한다. 다속성 태도점수를 산출하기 위해 피쉬바인(Fishbein) 방정식을 활용하고 피어슨 상관계수를 이용하여 사용자들간의 유사한 장소를 추출했다. 그리고 그룹렌즈의 선호도 예측 알고리즘을 활용하여 추천 대상 장소를 선정하고 유클라디안 거리법으로 사용자의 거리기반 장소를 추천하였다. 또한 본 논문에서는 실제 데이터를 이용한 실험을 통해 본 논문에서 제시한 시스템의 우수성도 입증하였다.

A study on the individual and group behavior based customer profile model for personalized products recommendation (개인화된 제품 추천을 위한 개인과 그룹 행동에 기반한 고객 프로파일 모델 연구)

  • Park Yu-Jin;Jang Geun-Nyeong;Jeong Yu-Jin
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2006.05a
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    • pp.1812-1818
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    • 2006
  • 일대일 마케팅을 실현하고 정보 과다 문제의 해결책으로 등장한 추천시스템의 다양한 기법을 적용하기 위해서는 고객의 관심 분야에 대한 정보인 고객 프로파일의 정의가 선행되어야 할 것으로 판단된다. 본 연구에서는 고객에게 개인화된 정보를 추천하기 위해 고객 개인의 행동과 그 고객이 속한 그룹의 행동 정보에 기반한 고객 프로파일 모델인 IGBCPM(Individual Group Behavior Customer Profile Model)을 제시한다.

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Mobile Context Based User Behavior Pattern Inference and Restaurant Recommendation Model (모바일 컨텍스트 기반 사용자 행동패턴 추론과 음식점 추천 모델)

  • Ahn, Byung-Ik;Jung, Ku-Imm;Choi, Hae-Lim
    • Journal of Digital Contents Society
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    • v.18 no.3
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    • pp.535-542
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    • 2017
  • The ubiquitous computing made it happen to easily take cognizance of context, which includes user's location, status, behavior patterns and surrounding places. And it allows providing the catered service, designed to improve the quality and the interaction between the provider and its customers. The personalized recommendation service needs to obtain logical reasoning to interpret the context information based on user's interests. We researched a model that connects to the practical value to users for their daily life; information about restaurants, based on several mobile contexts that conveys the weather, time, day and location information. We also have made various approaches including the accurate rating data review, the equation of Naïve Bayes to infer user's behavior-patterns, and the recommendable places pre-selected by preference predictive algorithm. This paper joins a vibrant conversation to demonstrate the excellence of this approach that may prevail other previous rating method systems.

Recommendation System Based on Correlation Analysis of User Behavior Data in Online Shopping Mall Environment (온라인 쇼핑몰 환경에서 사용자 행동 데이터의 상관관계 분석 기반 추천 시스템)

  • Yo Han Park;Jong Hyeok Mun;Jong Sun Choi;Jae Young Choi
    • KIPS Transactions on Computer and Communication Systems
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    • v.13 no.1
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    • pp.10-20
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    • 2024
  • As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

Tempo-oriented music recommendation system based on human activity recognition using accelerometer and gyroscope data (가속도계와 자이로스코프 데이터를 사용한 인간 행동 인식 기반의 템포 지향 음악 추천 시스템)

  • Shin, Seung-Su;Lee, Gi Yong;Kim, Hyoung-Gook
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.4
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    • pp.286-291
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    • 2020
  • In this paper, we propose a system that recommends music through tempo-oriented music classification and sensor-based human activity recognition. The proposed method indexes music files using tempo-oriented music classification and recommends suitable music according to the recognized user's activity. For accurate music classification, a dynamic classification based on a modulation spectrum and a sequence classification based on a Mel-spectrogram are used in combination. In addition, simple accelerometer and gyroscope sensor data of the smartphone are applied to deep spiking neural networks to improve activity recognition performance. Finally, music recommendation is performed through a mapping table considering the relationship between the recognized activity and the indexed music file. The experimental results show that the proposed system is suitable for use in any practical mobile device with a music player.

A Study on the Effect of Booth Recommendation System on Exhibition Visitors Unplanned Visit Behavior (전시장 참관객의 계획되지 않은 방문행동에 있어서 부스추천시스템의 영향에 대한 연구)

  • Chung, Nam-Ho;Kim, Jae-Kyung
    • Journal of Intelligence and Information Systems
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    • v.17 no.4
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    • pp.175-191
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    • 2011
  • With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

인터넷 쇼핑몰의 물류서비스 품질요인이 고객만족과 구매 후 행동에 미치는 영향에 관한 연구

  • 윤종훈;김광석;김용민
    • Proceedings of the Korea Association of Information Systems Conference
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    • 2005.12a
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    • pp.215-224
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    • 2005
  • 본 연구는 인터넷 쇼핑몰 기업들의 새로운 경쟁요인으로 부각하고 있는 물류서비스 부분의 품질 평가기준이나 평가척도 등에 관한 기존 연구를 현 시점에 맞게 재해석하고 또 새로운 평가 요소를 발굴하여 이를 인터넷 쇼핑몰에 적용함으로써 이용자들의 만족도, 재이용의도 및 타인추천의도의 향상을 꾀하였다. 특히 물류서비스 요인들의 고객만족에 대한 직접적인 영향을 분석함과 동시에 구매 후 행동인 재이용의도와 타인추천의도에 대한 간접적인 분석을 통해 그 영향 정도를 실증적으로 파악하여 쇼핑몰 운영자에게 있어 실질적인 도움을 주고자 하였다.

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