• Asia pacific journal of information systems
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• v.16 no.3
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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.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.309-316
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• 2016

While consumption patterns have changed online music, online music platform began to emerge. While people prefer popular music recommendation, they use the online music platform chart or use the SNS Platform to share information. Online platform Ranking is different because of different properties held by members. Meanwhile, we need music charts characteristics of SNS users. So there were a lot of attempts to chart a comprehensive variety of platforms. And continue to emerge theses linking the musical characteristics and SNS. In this paper, We have developed a new chart using the behavior of Twitter Users who listen to music, and studies comparing the results with existing chart.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.49-59
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• 2014

The Internet of Things(IoT) is a new promising technology made from a variety of technology. The IoT links the objects or people, then enabling anytime, anywhere connectivity for anything and not only for anyone. Social networking services have changed the way people communicate. Recently, new research challenges in many areas of Internet of things and social networking services are fired. In this paper, we propose things recommendation method using social relationship in social Internet of Things. We study previous researches about social network service, IoT, and social IoT. We proposed SIoT_FW(Social IoT Friendship Weight) using static and a dynamic social friendship weight. Also, our method considers four social relationships (Ownership Object Relationship, Co-Location Object Relationship, Social Object Relationship, Parental Object Relationship). We presents a music device scenario using our proposed method.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.707-712
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• 2017

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.43-52
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• 2013

In recent years, a large amount of music content is distributed in the Internet environment. In order to retrieve the music content effectively that user want, various studies have been carried out. Especially, it is also actively developing music recommendation system combining emotion model with MIR(Music Information Retrieval) studies. However, in these studies, there are several drawbacks. First, structure of emotion model that was used is simple. Second, because the emotion model has not designed for Korean language, there is limit to process the semantic of emotional words expressed with Korean. In this paper, through extending the existing emotion model, we propose a new emotion model KOREM(KORean Emotional Model) based on Korean. And also, we design and implement ontology using emotion model proposed. Through them, sorting, storage and retrieval of music content described with various emotional expression are available.

• Design Convergence Study
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• v.14 no.2
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• pp.151-168
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• 2015

As the market of music download stores moved into the mobile epoch the market was growing explosively. Recently, the digital music market tends to move from the mp3 download market to streaming service and the users can use the service on their mobile devices without reference to any inconvenient download and limited storage capacity. It was found that they mainly use the recommendation music playlist, instant player, main player and sharing of the functions of the streaming service from the user behavior research. This is noticeable features that set apart from the mp3 download service. However, the interface design of the streaming app followed the previous service and it needs the optimization of its UI design. In this study, the usability of high ranked three mobile streaming apps was evaluated. The result of the test was that Naver music and Bugs had high scores overall in four sections of the streaming service features. On the other hand, the Melon had primarily high score in color application on the service. The aim of this study is to suggest the direction of the UI design of music streaming service through the understanding of essence of streaming service and evaluation the usability test.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.1-12
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• 2022

As the music streaming service and the Hi-Fi market grow, various audio devices are being released. As a result, consumers have a wider range of product choices, but it has become more difficult to find products that match their musical tastes. In this study, we proposed a system that extracts the vocal component from the user's preferred sound source and recommends the most suitable audio device to the user based on this information. To achieve this, first, the original sound source was separated using Python's Spleeter Library, the vocal sound source was extracted, and the result of collecting frequency band data of manufacturers' audio devices was shown in a grid graph. The Matching Gap Index (MGI) was proposed as an indicator for comparing the frequency band of the extracted vocal sound source and the measurement data of the frequency band of the audio devices. Based on the calculated MGI value, the audio device with the highest similarity with the user's preference is recommended. The recommendation results were verified using equalizer data for each genre provided by sound professional companies.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.73-79
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• 2010

Emotion intelligence computing is able to processing of human emotion through it's studying and adaptation. Also, Be able more efficient to interaction of human and computer. As sight and hearing, music & image is constitute of short time and continue for long. Cause to success marketing, understand-translate of humanity emotion. In this paper, Be design of check system that matched music and image by user emotion keyword(irritability, gloom, calmness, joy). Suggested system is definition by 4 stage situations. Then, Using music & image and emotion ontology to retrieval normalized music & image. Also, A sampling of image peculiarity information and similarity measurement is able to get wanted result. At the same time, Matched on one space through pared correspondence analysis and factor analysis for classify image emotion recognition information. Experimentation findings, Suggest system was show 82.4% matching rate about 4 stage emotion condition.

• Informatization Policy
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• v.30 no.3
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• pp.92-121
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• 2023

As music streaming services (MSS) involve various interactions with users during the music consumption process, it is important to understand the user experience and manage the service accordingly. This study developed a user experience model for MSS by theoretically linking the quality characteristics considered important by music service users with the structure of user experience. PLS analysis was then performed using survey data to test the model. As a result, functionality (search, browsing, and personalized recommendation), UI usability, content quality (currentness, sufficiency, relevance), and monetary cost were found to be key experience factors that determine the experience consequence, i.e., user satisfaction. In addition, in a supplementary analysis comparing domestic and global services, differences in user experience were found between the two groups in terms of functionality and content quality. The user experience model of MSS proposed in this study serves as a new foundation for theory-based research in this field and provides meaningful implications for the competitive landscape among music streaming service platforms and for their competitive strategies.

• Asia Marketing Journal
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• v.22 no.3
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• pp.87-105
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• 2020

With the development of Information Technology, customers require promptly higher quality products and services. Companies try to make newly digital marketing strategies, but there are no empirical researches on them. This article empirically presents a new perspective that companies can shape the customer decision journey ahead by coordinating customer experience. In this article, based on Elaborated Likelihood Model (ELM) theory, customer experience consists of the emotional or cognitive experience. We surveyed about 200 subjects (N = 217) in their 20s and 30s based on the International Music Industry Association's Music Listening 2019 report, then analyzed four different models (before personalization-cognitive experience, before personalization-emotional experience, after personalization- cognitive experience, after personalization-emotional experience) by JASP and R Studio. We conducted Structural Equation Model (SEM) and paired t-test. Personalization factors are about recommendation systems in Spotify. The results of survey represent that companies can shape the Customer Decision Journey (CDJ) ahead especially through enhance cognitive experience. It empirically proves Elaborated Likelihood Model (ELM). The conclusion can be drawn that 'pulling' customer experience can be a new marketing strategies in the digital era.