• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.1-15
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• 2006

Currently the mobile web service is growing with a tremendous speed and mobile contents are spreading extensively. However, it is hard to search what the user wants because of some limitations of cellular phones. And the music is the most popular content, but many users experience frustrations to search their desired music. To solve these problems, this research proposes a hybrid recommendation system, MOBICORS-music (MOBIle COntents Recommender System for Music). Basically it follows the procedure of Collaborative Filtering (CF) system, but it uses Contents-Based (CB) data representation for neighborhood formation and recommendation of new music. Based on this data representation, MOBICORS-music solves the new item ramp-up problem and results better performance than existing CF systems. The procedure of MOBICORS-music is explained step by step with an illustrative example.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.195-200
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• 2015

Recently, most people use android based smartphones and we can find music players in any smartphones. However, it's hard to find a personalized music player which applies user's preference. In this paper, we propose an emotion-based music player, which analyses and classifies the music with user's emotion, recommends the music, applies the user's preference, and visualizes the music by color. Through the proposed music player, user could be able to select musics easily and use an optimized application.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.323-330
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• 2004

As mobile market grows more and more fast, the mobile contents market, especially music contents for mobile phones have recorded remarkable growth. In spite of this rapid growth, mobile web users experience high levels of frustration to search the desired music. New musics are very profitable to the content providers, but the existing collaborative filtering (CF) system can't recommend them. To solve these problems, we propose an extended CF system to reflect the user's real preference by representing the characteristics of users and musics in the feature space. We represent the musics using the music contents based acoustic features in multi-dimensional feature space, and then select a neighborhood with the distance based function. Furthermore, this paper suggests a recommendation for procedure for new music by matching new music with other users' preference. The suggested procedure is explained step by step with an illustration example.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.693-694
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• 2014

This short paper briefly describes the proposed music recommendation method that provides suitable music pieces to a listener depending on both listeners' ratings and content of music pieces. The proposed method consists of two methods. First, listeners' ratings prediction method is a combination the traditional user-based and item-based collaborative filtering methods. Second, genre classification method is a combination of feature extraction and classification procedures. The feature extraction step obtains audio signal information and stores it in data structure, while the second one classifies the music pieces into various genres using decision tree algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1487-1494
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• 2011

In this paper, a novel genre classification algorithm is proposed for music recommendation system. Especially, to improve the classification accuracy, the band-pass filter for octave-based spectral contrast (OSC) feature is designed considering the psycho-acoustic model and actual frequency range of musical instruments. The GTZAN database including 10 genres was used for 10-fold cross validation experiments. The proposed multiple-octave based OSC produces better accuracy by 2.26% compared with the conventional OSC. The combined feature vector based on the proposed OSC and mel-frequency cepstral coefficient (MFCC) gives even better accuracy.

• The Journal of Economics, Marketing and Management
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• v.12 no.1
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• pp.77-88
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• 2024

Purpose: By studying the effects of SNS advertising characteristics and SNS advertising music characteristics conducted by companies on advertising preference and advertising effects, we would like to suggest a plan for effective SNS advertising operation. Research design, data and methodology: In this study, a total of 483 surveys were collected for college student consumers in their 20s who had experience seeing advertisements on SNS, and 458 were used for the final analysis. In addition, the collected questionnaire data were analyzed using statistical programs SPSS 24.0 and AMOS 24.0, and Sobel Test was performed through structural equation modeling and regression analysis. Results: Advertising preference, purchase, and recommendation intentions increased as consumers who saw advertisements on SNS perceived the characteristics of advertisements (information, entertainment, individuality, and interactivity). However, advertising preference was not formed by SNS advertising music characteristics (fun, information delivery, unconscious stimulation, and emotional homogeneity). In addition, the higher the perception of SNS advertising music characteristics (fun, information delivery, unconscious stimulation, and emotional homogeneity), the more advertising effects such as purchase and recommendation intentions were linked, and the higher the perception of SNS advertising music characteristics (fun, information delivery, unconscious stimulation, and emotional homogeneity), the more advertising effects such as purchase and recommendation intentions could be created. Finally, it was confirmed that advertising preference had a partial mediating effect between SNS advertising characteristics and advertising effects, and between SNS advertising music characteristics and advertising effects. Conclusions: Unlike previous studies that have investigated the causal relationship of advertising effects according to sub-factors such as SNS advertising characteristics and SNS advertising music characteristics, it is significant in that it analyzes the variables used in the study as secondary factors.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.31-36
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• 2019

Music therapy, a field that convergence music and treatment, which play a fundamental role in personality formation, possesses diverse and complex treatment methods. Music therapists in charge of music therapy may experience the same phenomenon as countertransference in consultation with clients. In addition, experiencing psychological burnout, there are many difficulties in reaching the final goal of music therapy. In this paper, we provide a collaborative filtering-based music therapy consultation data recommendation model for smooth music therapy consultation with clients who visited for music therapy. The proposed model grasps the similarity between the conventional consultation data and the new consultant data through the euclidean distance algorithm. This is to recommend similar consultation materials. Since music therapists can provide optimal consultation materials for consultants who need music therapy, smooth consultation is expected.

• Journal of Information Technology and Architecture
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• v.10 no.2
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• pp.251-261
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• 2013

The appearance of various mobile Internet environment access to existing networks of mobile devices is easier to tell. In addition, mobile device users to use the wireless environment than a wired environment, user profile information is readily available features. Mobile devices have features that use alone. These characteristics of mobile devices to apply the personalization service is the best system. This paper proposes for mobile device users a personalized mobile music content recommendation service. This service propose to utilizes the user's access history information using time-weighting and collaborative filtering. Access history information can find out information of user interest. Using this information, consider the preference of music genre and time-weighted based on the recommendations makes the music. This method the problem of the traditional music recommendation system, point user's favorite music genre is changing over time do not consider that to solve the problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.504-510
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• 2009

Recently, personalized-adaptive services became the center of interest in the world. However the services about music are not widely diffused out. That is because the analyzing of music information is more difficult than analyzing of text information. In this paper, we propose a music recommendation system which provides personalized services. The system keeps a user's listening list and analyzes it to select pieces of music similar to the user's preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a piece of music is mapped into a point in the property space and the time is converted into the weight of the point. At this time, if we select and analyze the group which is selected by user frequently, we can understand user's taste. However, it is not easy to predict how many groups are formed. To solve this problem, we apply the K-means clustering algorithm to the weighted points. We modified the K-means algorithm so that the number of clusters is dynamically changed. This manner limits a diameter so that we can apply this algorithm effectively when we know the range of data. By this algorithm we can find the center of each group and recommend the similar music with the group. We also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the user's preference. We perform experiments with one hundred pieces of music. The result shows that our proposed algorithm is effective.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.