• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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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.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.11
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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.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.31-35
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• 2021

In recent years, due to COVID-19, the process of shopping has become more restricted and difficult for customers. Based on this aspect, customers are more interested in online shopping to keep the Untact rules and stay safe, similarly ordering their product based on their need and interest with most straightforward and fastest ways. In this paper, the reinforcement learning technique is applied in the product recommendation system to improve the recommendation system quality for better and more related suggestions based on click patterns and users' profile information. The dataset used in this system was taken from an online shopping mall in Jeju island, South Korea. We have compared the proposed method with the recent state-of-the-art and research results, which show that reinforcement learning effectiveness is higher than other approaches.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.19-24
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• 2020

Recently, due to the rapid increase of pets, there is a need for an integrated pet-related personalized product recommendation service such as feed recommendation using a health status check of pets and various collected data. This paper implements a product recommendation system that can perform various personalized services such as collection, pre-processing, analysis, and management of pet-related data using big data. First, the sensor information worn by pets, customer purchase patterns, and SNS information are collected and stored in a database, and a platform capable of customized personalized recommendation services such as feed production and pet health management is implemented using statistical analysis. The platform can provide information to customers by outputting similarity product information about the product to be analyzed and information, and finally outputting the result of recommendation analysis.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.19-27
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• 2011

Recommendation systems are developed to overcome the problems of selection and to promote intention to use. In this study, we propose a recommendation system using adjacency data according to user's behavior over time. For this, the product adjacencies are identified from the adjacency matrix based on graph theory. This research finds that there is a trend in the users' behavior over time though product adjacency fluctuates over time. The system is tested on its usability. The tests show that implementing this recommendation system increases users' intention to purchase and reduces the search time.

• Asia pacific journal of information systems
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• v.29 no.2
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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.

• Journal of Information Processing Systems
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• v.17 no.2
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.914-915
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• 2017

Importance and usage of the recommender system increases with the increase of information. The accuracy of the system recommendation primarily depends on the data. There is a problem in recommender systems, known as cold start problem. The lack of data about new products and users causes the cold start problem, and the system will not be able to give correct recommendation. This paper deals with cold start problem by comparing product specification and the review of the resembled products. The user, who likes the resembled product of the new one has more probability of taking interest in the new product as well. However, if a user disagreed with resembled product due to some reasons which the user mentioned in the reviews. The new product overcomes that issue, so the user will greatly accept the new product. Therefore, the system needs to recommend new product to those users as well, in this way the cold start problem will get resolved.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.91-105
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• 2007

This research suggests a recommendation system that enables bidirectional communications between the user and system using a utility range-based product recommendation algorithm in order to provide more dynamic and personalized recommendations. The main idea of the proposed algorithm is to find the utility ranges of products based on user specified preference information and calculate the similarity by using overlapping probability of two range values. Based on the probability, we determine what products are similar to each other among the products in the product list of collaborative companies. We have also developed a Web-based application system to recommend similar products to the customer. Using the system, we carry out the experiments for the performance evaluation of the procedure. The experimental study shows that the utility range-based approach is a viable solution to the similar product recommendation problems from the viewpoint of both accuracy and satisfaction rate.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.1027-1038
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• 2006

There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.