• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1129-1143
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• 2009

This paper proposes a recommendation system (Association Rule System for Targeting) which considers target which is not considered by previous Logistic Regression system, and proves that the efficiency of the recommendation system is better than that of the current and previous Apriori algorithm system. Also this study shows that the click and purchasing rate of the proposed Association Rule System for Targeting is much higher than those of current Apriori algorithm system after the purchasing campaign even though the open rate of the former is lower than that of the latter. In comparison with Logistic Regression methodology, this paper proves with experimental data that the purchasing effect of the proposed system for specific items is much higher in accuracy than that of current Apriori algorithm system even though the purchasing rate of current Apriori algorithm system is higher in whole shopping malls than that of the proposed Association Rule System for Targeting.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.573-576
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• 2014

In this paper, it provides social network service using context-aware for tourism. For this the service requires Anthropomorphic natural process. The service object need to provide the function analyzing, storing and processing user action. In this paper, it provides an algorithm to analysis with personalized context aware for users. Providing service is an algorithm providing social network, helped by 'Friend recommendation algorithm' which to make relations and 'Attraction recommendation algorithm' which to recommend somewhere significant. Especially when guide is used, server analysis history and location of users to provide optimal travel path, named 'Travel path recommendation algorithm'. Such as this tourism social network technology can provide more user friendly service. This proposed tour guide system is expected to be applied to a wider vary application services.

• Information Systems Review
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• v.25 no.4
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• pp.67-87
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• 2023

Many online shopping sites now offer personalized recommendation systems to improve consumers' shopping experiences by lowering costs (time, cost, etc.), catering to consumers' tastes, and stimulating consumers' potential shopping needs. So far, domestic and foreign research on the personalized recommendation system has mainly focused on the field of computer science, which is advantageous for obtaining accurate personalized recommendation results for users but difficult to continuously track the users' psychological states or behavioral intentions. This study attempted to investigate the effect of the characteristics of the personalized recommendation system in the online shopping environment on consumer perception and purchase intention for consumers using the Stimulus-Organism-Response (S-O-R) model. The analysis results adopted all hypotheses on the effect of the quality of the personalized recommendation system and information quality on trust and perceived value. Through the empirical results of this study, the factors influencing consumers' use of personalized recommendation system can be identified. In order to increase more purchase, online shopping companies need to understand consumers' tastes and improve the quality of the personalized system by improving the recommendation algorithm thus to provide more information about products.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.283-308
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• 2012

With explosive growth of information by recent advancements in information technology and the Internet, users need a method to acquire appropriate information. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Also, users and service providers are growing more and more interested in personalized information recommendation. This study designed and implemented personalized information recommendation system based on AR as a method to provide positive information service for information users as a method to provide positive information service. To achieve the goal, the proposed method overcomes the weaknesses of existing systems, by providing a personalized recommendation method for contents that works in a large-scaled data and user environment. This study based on the proposed method to extract rules from log files showing users' behavior provides an effective framework to extract Association Rule.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.625-632
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• 2001

The electronic stores have realized that they need to understand their customers and to quickly response their wants and needs. To be successful in increasingly competitive Internet marketplace, recommender systems are adapting data mining techniques. One of most successful recommender technologies is collaborative filtering (CF) algorithm which recommends products to a target customer based on the information of other customers and employ statistical techniques to find a set of customers known as neighbors. However, the application of the systems, however, is not very suitable for seasonal products which are sensitive to time or season such as refrigerator or seasonal clothes. In this paper, we propose a new adjusted item-based recommendation generation algorithms called the exponentially weighted collaborative filtering recommendation (EWCFR) one that computes item-item similarities regarding seasonal products. Finally, we suggest the recommendation system with relatively high quality computing time on main memory database (MMDB) in XML since the collaborative filtering systems are needed that can quickly produce high quality recommendations with very large-scale problems.

• 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 Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.113-128
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• 2022

The objective of this study is to pay attention to the personal media environment that is in the center of rapid changes in the media industry, to especially explore the activity area of one-person or minority media creators who lead the mobile media environment that could be connected, watched, and produced anywhere, and to closely examine the mutual ecosystem between creators and viewers. Especially, paying attention to the recommendation service YouTube provides, for example, based on the big data algorithm related to users' habitual use, when users' data used are provided more, the users face the advanced service, this study aimed to examine the effects of recommendation service on the formation of trust between user and producer, user flow, and subscription intention, and also to demonstrate the process of forming this mutual relation through concrete data. In the conclusion, implications that can be inferred based on the research results and suggestions for further research in the future were presented.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.79-98
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• 2011

This paper proposes a semantic recommendation technique for a personalized e-commerce portal. Semantic recommendation is achieved by utilizing the attributes of products. The semantic similarity of the products is merged with the rating information of the products to provide an accurate recommendation. The recommendation technique also analyzes various attitudes of the customer to evaluate the implicit rating of products. Attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information." We implicitly track customer attitude to estimate the rating of products for recommending products. Also we implement a session validation process to identify the valid sessions that are highly important for giving an accurate recommendation. Our recommendation technique shows a high degree of accuracy as we use age groupings of customers with similar preferences. The experimental section shows that our proposed recommendation method outperforms well known collaborative filtering methods not only for the existing customer, but also for the new user with no previous purchase record.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.