• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.53-60
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• 2008

Recently, the application of internet services has been realized by effort for an offer of various informations and personalization services. So in this paper, we propose the web service application model for service integration and personalization based on internet multimedia subsystems. For this services integration and personalization, we need to establish a policy of supporting personalization service. And It's required to analyze information of service users and to extract the components for the personalization services. With the process, It will provide the efficient integration between the exist services and the external services and It can be realized detail services for personal with construction of new services model.

• Archives of design research
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• v.16 no.2
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• pp.23-34
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• 2003

The need for personalized web site design has been increased these days. Current approach for personalized web site design is easily applied to web site with their cost-effective feature, but is hard to provide a more refined personalized service due to its lack of accumulation of user data. In this study, the design recommender system is investigated as a more advanced method for web site design personalization. We provide an overview of current recommender systems, and then outlined a newly developed design recommender system, which employs collaborative filtering technique to provide tailored recommendation for users.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.5
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• pp.541-550
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• 2003

It is important for the strategy of product sales to investigate the consumer's sensitivity and preference degree in the environment that the process of material development has been changed focusing on the consumer renter. In the present study, we propose the Fashion Design Recommender System (FDRS) of textile design applying collaborative filtering personalization technique as one of methods in the material development centered on consumer's sensibility and preferences. In collaborative filtering personalization technique based on textile, Pearson Correlation Coefficient is used to calculate similarity weights between users. We build the database founded on the sensibility adjective to develop textile designs by extracting the representative sensibility adjective from users' sensibility and preferences about textile designs. FDRS recommends textile designs to a consumer who has a similar propensity about textile. Ultimately, this paper sugeests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommender System (FDRS)

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.1-21
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• 2015

This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.

• Asia pacific journal of information systems
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• v.27 no.2
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• pp.126-138
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• 2017

The proliferation of items increased the difficulty of customers in finding the specific items they want to purchase. To solve this problem, companies adopted recommender systems, such as collaborative filtering systems, to provide personalization services. However, companies use only meaningful and essential data given the explosive growth of data. Some customers are concerned that their private information may be exposed because CF systems necessarily deal with personal information. Based on these concerns, we analyze the effects of the amount of information on recommendation performance. We assume that a customer could choose to provide overall information or partial information. Experimental results indicate that customers who provided overall information generally demonstrated high performance, but differences exist according to the characteristics of products. Our study can provide companies with insights concerning the efficient utilization of data.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.5-33
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• 2018

In this paper, we analyze a user's usage behavior, identify and track search intention and interest field based on the National Science and Technology Standard Classification, and use it to personalize science and technology information. In other words, we sought to satisfy both efficiency and satisfaction in searching for information that users want by improving scientific information search performance. We developed the personalization service of science and technology information and evaluated the suitability and usefulness of personalized information by comparing the search performance between expert experimental group and control group. As a result, the personalization service proposed in this study showed better search performance than comparative service and proved to provide higher usability.

• Korean Journal of Culture and Social Issue
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• v.19 no.3
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• pp.487-506
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• 2013

The present study investigated whether do the efforts of interviewer really impact on the building a rapport by using four rapport building techniques-personalization, empathy, listening, and credibility. One woman probation officer interviewed 139 criminals(male, 122; female, 17). And she tried to building a rapport by using 4 rapport building techniques for about 11 minutes in every interview. In result, the degree of rapport perceived by interviewees was different significantly between high rapport group and low rapport group of 4 each rapport technique. Result suggests that personalization and listening techniques would be efficient way of building a rapport in investigative interviewing.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.75-85
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• 2017

Personalization recommendations have already proven in many areas of the e-commerce industry. For personalization recommendations, additional work such as reclassifying items is generally necessary, which requires personal information. In this study, we propose a recommendation technique that neither exploit personal information nor reclassify items. We focus on music recommendation and performed experiments with actual music listening data. Experimental analysis shows that the proposed method may result in meaningful recommendations albeit it exploits less amount of data. We analyze the appropriate number of items and present future considerations for contextual recommendation.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.11
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• pp.475-482
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• 2013

Several approaches to recommendation systems have been studied. One of the most successful technologies for building personalization and recommendation systems is collaborative filtering, which is a technique that provides a process of filtering customer information based on such information profiles. Collaborative filtering systems, however, have a sparsity if there is not enough data to recommend. In this paper, we suggest a movie recommendation system, based on the weighted personal propensity and the collaborating filtering system, in order to provide a solution to such sparsity. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a weighted personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the optimal personalization factors.