• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.427-433
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• 2024

This study aims to develop a personalized music digital therapeutic based on MBTI personality types and apply it to depression treatment. In the data collection stage, participants' MBTI personality types and music preferences were surveyed to build a database, which was then preprocessed as input data for the KNN model. The KNN model calculates the distance between personality types using Euclidean distance and recommends music suitable for the user's MBTI type based on the nearest K neighbors' data. The developed system was tested with new participants, and the system and algorithm were improved based on user feedback. In the final validation stage, the system's effectiveness in alleviating depression was evaluated. The results showed that the MBTI personality type-based music recommendation system provides a personalized music therapy experience, positively impacting emotional stability and stress reduction. This study suggests the potential of nonpharmacological treatments and demonstrates that a personalized treatment experience can offer more effective and safer methods for treating depression.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.177-187
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• 2024

177-Existing music recommendation systems do not sufficiently consider the discrepancy between the intended emotions conveyed by song lyrics and the actual emotions felt by users. In this study, we generate topic vectors for lyrics and user comments using the LDA model, and construct a user preference model by combining user behavior trajectories reflecting time decay effects and playback frequency, along with statistical characteristics. Empirical analysis shows that our proposed model recommends music with higher accuracy compared to existing models that rely solely on lyrics. This research presents a novel methodology for improving personalized music recommendation systems by integrating emotion recognition and user behavior analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1435-1442
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• 2009

This paper proposes a music recommendation system for a driver in vehicle. The proposed system provides (selects and plays) a music to a driver in vehicle in real-time manner by inferring his preference based on physical, environmental, and personal information. Pulse data as physical information, age and biorhythm as personal information, and time as environmental information are used to infer a driver's and thus recommend a music. Experimental results showed that the proposed system could provide better satisfaction to a driver on the recommended music compared to the conventional approach.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.650-657
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• 2006

The music is a part of our daily life in these days. And when the people listen to the music, they are affected by the context. However, previous researches on the music recommendation system have the problem that they didn't consider the proper contextual information efficiently. They only used the content-based filtering or the method to use musical metadata (genre, artist, etc.). Recently, there are some researches about the music recommendation system which applies the status(temperature, humidity, etc.) of environments. But, it is difficult to be accepted by the contextual information. Therefore, we propose the music recommendation system that is dynamically applied by the contextual information as well as the metadata in the previous researches. And the system can provide users with the music that they want to listen to, and then the users can be more satisfied. Also, the services can be improved by the feedback of the users. In order to solve this problem, the context-information for selecting a music list is defined and the music recommendation system is designed by using the content-based filtering method. The system is suitable for the user's taste and the context. The music recommendation system we are proposing uses an OSGi framework in the home network. As a result, the satisfaction of users and the quality of services will be improved more efficiently by supporting the mobility of services as well as the distributed processing.

• 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.

• 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.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.111-126
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• 2006

The context-awareness is one of the core technologies in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a case-based music recommendation system. Our proposed system consists of Intention Module and Recommendation Module. The Intention Module infers whether a user wants to listen to the music or not from the environmental context information. Then, the Recommendation Module selects songs from the songs that are listened by similar users in similar context, and recommends them to the user. The results showed that our proposed system outperformed the traditional case-based music recommendation system in accuracy by about 9% point.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.159-165
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• 2015

The need for the recognition of music emotion has become apparent in many music information retrieval applications. In addition to the large pool of techniques that have already been developed in machine learning and data mining, various emerging applications have led to a wealth of newly proposed techniques. In the music information retrieval community, many studies and applications have concentrated on tag-based music recommendation. The limitation of music emotion tags is the ambiguity caused by a single music tag covering too many subcategories. To overcome this, multiple tags can be used simultaneously to specify music clips more precisely. In this paper, we propose a novel technique to rank the proper tag combinations based on the acoustic similarity of music clips.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.108-118
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• 2010

In this paper, we propose Context-based Music Recommendation (COMUS) ontology for modeling user's musical preferences and context and for supporting reasoning about the user's desired emotion and preferences. The COMUS provides an upper Music Ontology that captures concepts about the general properties of music such as title, artists and genre and also provides extensibility for adding domain-specific ontologies, such as Mood and Situation, in a hierarchical manner. The COMUS is music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology. Using this context ontology, we believe that the use of logical reasoning by checking the consistency of context information, and reasoning over the high-level, implicit context from the low-level, explicit information. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the application. We present some of the experiments we performed as a case-study for music recommendation.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.479-487
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• 2016

People can listen to almost every type of music by music streaming services without possessing music. Ironically it is difficult to choose what to listen to. A music recommendation system helps people in making a choice. However, existing recommendation systems have high computation complexity and do not consider context information. Emotion is one of the most important context information of music. Lyrics can be easily computed with various language processing techniques and can even be used to extract emotion of music from itself. We suggest a music-level similarity evaluation method using emotion and structure. Our result shows that it is important to consider semantic information when we evaluate similarity of music.