• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.244-246
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• 2002

The rapid increase of E-commerce market requires a solution to assist the buyer to find his or her interested items. The intelligent agent model is one of the approaches to help the buyers in purchasing items in outline market. In this paper, the user query processing model in the item recommendation agent is proposed. In the proposed model, the retrieval result is affected by the automatically generated queries from user preference information as well as the queries explicitly given by user. Therefore, the proposed model can provide the customized search results to each user.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.502-503
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• 2019

In the existing system that recommends through review data analysis, it does not reflect personal preference details such as user's characteristics or product purchase tastes, in this paper, we propose a system that provides customized recommendation information to various users by selecting the criterion that the user thinks most importantly when searching for the product and purchasing the product, and analyzing it. This is because the user's personal preference is reflected by arranging the product list based on the criterion that the user occupies the largest portion of the product purchase, so that it is more efficient than the recommendation through the recommendation system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.1-8
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• 2023

Cross-domain recommendation is a method that shares related user information data and item data in different domains. It is mainly used in online shopping malls with many users or multimedia service contents, such as YouTube or Netflix. Through K-means clustering, embeddings are created by performing clustering based on user data and ratings. After learning the result through a transformer network, user satisfaction is predicted. Then, items suitable for the user are recommended using a transformer-based recommendation model. Through this study, it was shown through experiments that recommendations can predict cold-start problems at a lesser time cost and increase user satisfaction.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.1-6
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• 2024

In this paper, we develop an AI-based recommendation system that matches the specifications of smartphones from company 'S'. The system aims to simplify the complex decision-making process of consumers and guide them to choose the smartphone that best suits their daily needs. The recommendation system analyzes five specifications of smartphones (price, battery capacity, weight, camera quality, capacity) to help users make informed decisions without searching for extensive information. This approach not only saves time but also improves user satisfaction by ensuring that the selected smartphone closely matches the user's lifestyle and needs. The system utilizes unsupervised learning, i.e. clustering (K-MEANS, DBSCAN, Hierarchical Clustering), and provides personalized recommendations by evaluating them with silhouette scores, ensuring accurate and reliable grouping of similar smartphone models. By leveraging advanced data analysis techniques, the system can identify subtle patterns and preferences that might not be immediately apparent to consumers, enhancing the overall user experience. The ultimate goal of this AI recommendation system is to simplify the smartphone selection process, making it more accessible and user-friendly for all consumers. This paper discusses the data collection, preprocessing, development, implementation, and potential impact of the system using Pandas, crawling, scikit-learn, etc., and highlights the benefits of helping consumers explore the various options available and confidently choose the smartphone that best suits their daily lives.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.257-268
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• 2008

Multimedia recommendation systems analyze user preferences and recommend items(multimedia contents) to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering(CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. If there are few known preferences for a user, it is difficult to find many similar users, and therefore the performance of recommendation is degraded. This problem is more serious when a new user is first using the system. In this paper, we propose a method of generating additional feature of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. In our method, we first generate additional features by using the probability distribution of feature values, then recommend items by applying collaborative filtering on the modified data to include additional features. Several experimental results that show the effectiveness of the proposed method are also presented.

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.276-279
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• 2004

The location-based service is one of the most powerful services in the mobile area. The location-based service provides information service for moving user's location information and information service using wire / wireless communication. In this paper, we propose a model for personalized recommendation system which includes location information and personalized recommendation system for location-based service. For this service system, we consider mobile clients that have a limited resource and low bandwidth. Because it is difficult to input the words at mobile device, we must deliberate it when we design the interface of system. We design and implement the personalized recommendation system for location-based services(advertisement, discount news, and event information) that support user's needs and location information. As a result, it can be used to design the other location-based service systems related to user's location information in mobile environment. In this case, we need to establish formal definition of moving objects and their temporal pattern.