• Asia pacific journal of information systems
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• 제29권2호
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• pp.203-216
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• 2019

There is an accuracy-diversity dilemma with personalized recommendation services. Some researchers believe that accurate recommendations might reinforce customer satisfaction. However, others claim that highly accurate recommendations and customer satisfaction are not always correlated. Thus, this study attempts to establish the causal factors that determine customer satisfaction with personalized recommendation services to reconcile these incompatible views. This paper employs statistical analyses of simulation to investigate an accuracy-diversity dilemma with personalized recommendation services. To this end, we develop a personalized recommendation system and measured accuracy, diversity, and customer satisfaction using a simulation method. The results show that accurate recommendations positively affected customer satisfaction, whereas diverse recommendations negatively affected customer satisfaction. Also, customer satisfaction was associated with the recommendation product size when neighborhood size was optimal in accuracy. Thus, these results offer insights into personalizing recommendation service providers. The providers must identify customers' preferences correctly and suggest more accurate recommendations. Furthermore, accuracy is not always improved as the number of product recommendation increases. Accordingly, providers must propose adequate number of product recommendation.

• 대한안전경영과학회지
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• 제10권4호
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• pp.309-314
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• 2008

Many studies on predicting and recommending information and products have been studying to meet customers' preference. Unnecessary information should be removed to satisfy customers' needs in massive information. The some information filtering methods to remove unnecessary information have been suggested but these methods have scarcity and scalability problems. Therefore, this paper explores a personalized recommendation system based on artificial neural network (ANN) to solve these problems. The insurance product recommendation is adapted as an example to demonstrate the proposed method. The proposed recommendation system is expected to recommended a suitable and personalized insurance products for customers' satisfaction.

• 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.

• 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.

• 디지털산업정보학회논문지
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• 제18권4호
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• pp.85-96
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• 2022

Personalized recommendation technology is one of the most important technologies in electronic commerce environment. It helps users overcome information overload by suggesting information that match user's interests. In e-commerce environment, both mobile device users and smart device users have risen dramatically. It creates new challenges. Our method suggests product information that match user's device interests beyond only user's interests. We propose a device-centered personalized recommendation method. Our method uses both purchase and share behavior for user's devices interests. Moreover, it considers data type preference for each device. This paper presents a new recommendation method and algorithm. Then, an e-commerce scenario with a computer, a smartphone and an AI-speaker are described. The scenario shows our work is better than previous researches.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2002년도 춘계학술대회 논문집
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• pp.342-351
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• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.

• Asia pacific journal of information systems
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• 제16권3호
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• pp.159-176
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• 2006

Collaborative filtering is one of the popular techniques for personalized recommendation in e-commerce. In collaborative filtering, user profiles are usually managed per product category in order to reduce data sparsity. Product diversification of Internet storefronts and multiple product category sales of e-commerce portals require cross-product category usage of user profiles in order to overcome the cold start problem of collaborative filtering. In this paper, we study the feasibility of cross-product category usage of user profiles, and suggest a method to improve recommendation performance of cross-product category user profiling. First, we investigate whether user profiles on a product category can be used to recommend products in other product categories. Furthermore, a way of utilizing user profiles selectively is suggested to increase recommendation performance of cross-product category user profiling. The feasibility of cross-product category user profiling and the usefulness of the proposed method are tested with real click stream data of an Internet storefront which sells multiple product categories including books, music CDs, and DVDs. The experiment results show that user profiles on a product category can be used to recommend products in other product categories. Also, the selective usage of user profiles based on correlations between subcategories of two product categories provides better performance than the whole usage of user profiles.

• Asia pacific journal of information systems
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• 제15권1호
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• pp.63-79
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• 2005

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation system, WebCF-PT based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of traditional CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. A prototype recommendation system, WebCF-PT is developed and Internet shopping mall, EBIB(e-Business & Intelligence Business) is constructed to test the WebCF-PT system.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)

• 한국융합학회논문지
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• 제11권11호
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• pp.19-24
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• 2020

최근, 애완동물의 급격한 증가로 애완동물의 건강상태 체크와 다양하게 수집된 데이터를 활용하여 사료 추천 등 통합적인 애완동물관련 개인화 상품 추천 서비스가 요구된다. 본 논문은 빅 데이터 기술을 활용하여 애완동물관련 데이터 수집, 전처리, 분석, 관리등 다양한 개인화서비스를 할 수 있는 상품 추천시스템을 구현한다. 먼저, 애완동물이 착용하고 있는 센서 정보와 고객의 구매 패턴, SNS 정보를 수집해 데이터베이스에 저장하고 통계적 분석을 활용하여 사료제작, 애완동물 건강관리 등 맞춤형 개인화 추천 서비스가 가능한 플랫폼을 구현한다. 본 플랫폼은 유사도가 분석될 상품과 상품정보에 대한 유사도 상품 정보를 출력하고 최종적으로 추천 분석한 결과를 출력하여 고객에게 정보를 제공 할 수 있다.