• Journal of Information Technology Applications and Management
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• 제26권6호
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.599-614
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• 2021

This paper proposes a digital signage system based on an intelligent recommendation model. The proposed system consists of a server and an edge. The server manages the data, learns the advertisement recommendation model, and uses the trained advertisement recommendation model to determine the advertisements to be promoted in real time. The advertisement recommendation model provides predictions for various products and probabilities. The purchase index between the product and weather data was extracted and reflected using correlation analysis to improve the accuracy of predicting the probability of purchasing a product. First, the user information and product information are input to a deep neural network as a vector through an embedding process. With this information, the product candidate group generation model reduces the product candidates that can be purchased by a certain user. The advertisement recommendation model uses a wide and deep recommendation model to derive the recommendation list by predicting the probability of purchase for the selected products. Finally, the most suitable advertisements are selected using the predicted probability of purchase for all the users within the advertisement range. The proposed system does not communicate with the server. Therefore, it determines the advertisements using a model trained at the edge. It can also be applied to digital signage that requires immediate response from several users.

• Journal of Information Technology Applications and Management
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• 제29권3호
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.369-384
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• 2021

In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low- and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.

• 한국의류학회지
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• 제36권11호
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• pp.1222-1236
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• 2012

This study examined the effects of product recommendation services as an atmosphere for online mass customization shopping sites on consumers' cognitive and affective responses. We conducted a between-subject experimental study using a convenience sample of college students. A total of 196 participants provided usable responses for structural equation modeling analysis. The findings of the study support the S-O-R model for a product recommendation system as an element of the shopping environment with an influence on OMC product evaluations and arousal. The results showed that OMC product recommendation service positively affected cognitive and affective responses. The findings of the study suggest that OMC retailers might pay attention to the affective and cognitive responses of consumers through product recommendation services that can enhance product evaluations and OMC usage intentions.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.265-274
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• 2021

본 논문에서는 검색 키워드와 상품 상세정보를 활용한 Doc2vec 기반의 새로운 추천 모형을 제안한다. 지금까지 추천 시스템에 관한 많은 기존 연구에서는 고객의 구매 이력이나 평점 같은 정형 데이터만을 사용하는 협업 필터링(CF) 알고리즘에 기반한 추천 모델이 제안되었다. 그러나 CF에서 온라인 고객 리뷰와 같은 비정형 데이터를 사용하면, 보다 나은 추천결과를 도출할 수 있다. 이에 본 연구에서는 기존 연구에서 거의 활용되지 않았던 검색 키워드 정보와 상품 상세정보를 제품 추천에 활용할 것을 제안한다. 본 연구의 제안 모형은 고객이 구매한 상품에 대한 평점, 검색어, 상품 상세정보를 종합적으로 고려한 CF 알고리즘을 이용해 추천결과를 생성한다. 이 때 비정형 데이터로부터 정량적인 패턴을 추출하기 위한 방법으로는 Doc2vec이 적용된다. 실험 결과 제안 모형이 기존 추천 모형보다 더 나은 성능을 보이는 것을 알 수 있었고, 검색어 및 상품 상세정보가 추천에 유의한 영향을 미치는 것을 확인하였다. 본 연구는 고객의 온라인 행동 정보를 추천시스템에 적용하였다는 점과 전통적인 CF의 한계 중 하나인 콜드 스타트 문제를 완화하였다는 점에서 학술적 의의가 있다.

• 지능정보연구
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• 제12권4호
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• pp.1-14
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• 2006

본 연구는 상품추천을 위해 필요한 고객 선호도 추정모형(Customer Preference Estimation Model)을 제안하고, 이러한 선호도 추정결과에 따른 선호도 정보를 이용하여 궁극적으로 상품추천의 성과를 제고시키기 위한 방법을 제시하였다. 즉, 제품에 대한 고객 선호 영향요인들과 고객 선호도와의 관계를 모형화 함으로써 고객 선호도를 보다 더 정확히 추정할 수 있는 새로운 선호도 추정모형을 제안하였다. 이 제안모형은 선호도 영향요인들의 상대적인 가중치를 선호도 최적화 학습을 통해 도출함으로써, 보다 정확한 선호도 측정을 가능하게 해 준다. 한편, 이 모형의 타당성을 검증하기 위해서 본 연구에서는 가상서점 고객들을 대상으로 고객 선호도 정보를 수집한 후, 본 제안모형을 적용했을 때의 협업 필터링의 추천성과와 사전가중치 부여방식인 기존 선호도 계산식을 이용했을 경우의 추천성과를 비교 분석하였다. 이에 대한 실증분석 결과는 본 연구에서 제안한 선호도 추정모형을 적용했을 때의 협업 필터링의 성과가 기존 선호도 계산방식을 적용했을 때의 협업 필터링의 성과보다 더 우수한 것으로 나타났다.

• Journal of Information Technology Applications and Management
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• 제25권2호
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• 패션비즈니스
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• 제27권3호
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.332-339
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• 2005

We developed a similar product recommendation system for implementing a collaborative commerce model between the cooperating companies. The system is based on a similar product finding algorithm. The main idea of the proposed algorithm is using a multi-attribute decision making(MADM) to find the utility values of products in same product class of the companies. Based on the values we determine what products are similar. The system helps the companies to recommend products in accordance with the customer's preferences regarding product specifications.