Music Recommendation Method Research Articles

Research on music culture personalized recommendation based on factor decomposition machine

The emergence of Internet music has slowed down the restrictions of space and time on people’s enjoyment of music information services. However, in the face of massive and growing music works, information overload has become the most direct problem, and the need to improve user experience has become very urgent. One of the effective solutions to information overload is recommender system, which can help people to discover the interesting content from the complicated information. Therefore, the combination of recommendation system and Internet music has become an inevitable trend of music development. Referring to the traditional music recommendation methods, this paper proposes a big data music personalized recommendation method based on big data analysis, which combines user behavior, behavior context, user information, and music work information. In this paper, the user big data is introduced into the model building process. Through the factor decomposition machine (FM) learning method, the effect of various influencing factors on user behavior is analyzed to build the user dynamic interest model and complete the user preference acquisition. In the stage of recommendation candidate set selection, combining with the traditional collaborative filtering recommendation idea, the work of recommendation candidate set selection is carried out from two aspects. At the same time, this paper designed and completed a comparative experiment with the processing performance, accuracy and coverage as indicators, and verified the effectiveness of the improved recommendation method.

Improving music recommendation by incorporating social influence

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

소셜네트워크 기반 음악 추천시스템

Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user`s current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user`s recent emotion or current situation by analyzing user`s social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user`s current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

A Music Recommendation Method Using Emotional States by Contextual Information

User's selection of music is largely influenced by private tastes as well as emotional states, and it is the unconsciousness projection of user's emotion. Therefore, we think user's emotional states to be music itself. In this paper, we try to grasp user's emotional states from music selected by users at a specific context, and we analyze the correlation between its context and user's emotional state. To get emotional states out of music, the proposed method extracts emotional words as the representative of music from lyrics of user-selected music through morphological analysis, and learns weights of linear classifier for each emotional features of extracted words. Regularities learned by classifier are utilized to calculate predictive weights of virtual music using weights of music chosen by other users in context similar to active user's context. Finally, we propose a method to recommend some pieces of music relative to user's contexts and emotional states. Experimental results shows that the proposed method is more accurate than the traditional collaborative filtering method.

Social mix: automatic music recommendation and mixing scheme based on social network analysis

General preferences for music change over time. Moreover, music preferences depend on diverse factors, such as language, people, location, and culture. This dependency should be carefully considered to provide satisfactory music recommendations. Presently, typical music recommendations simply involve providing a list of songs that are then played sequentially or randomly. Recently, there has been an increasing demand for new music recommendation and playback methods. In this paper, we propose a scheme for recommending music automatically by considering both personal and general musical predilections, and for blending such music into a mixed clip for seamless playback. For automatic music recommendations, we first analyze social networks to identify a general predilection for certain music genres that depends on time and location. Songs that are generally preferred within a certain time period and location are identified through statistical analysis. This is done by analyzing, filtering, and storing massive social network streams into our own database in real time. In addition, a personal predilection for certain music genres can be inferred by analyzing similar user relationships in social network services. We selected such music based on instant graphs that are generated by user relationships and underlying music information. After the songs are selected, an automatic music mixing method is used to blend those songs into a continuous music clip. We implemented a prototype system and experimentally confirmed that our scheme provides satisfactory results.

2B2-1 次世代カラオケサービスへの応用をねらいとした楽曲リコメンド手法の提案

2B2-1 次世代カラオケサービスへの応用をねらいとした楽曲リコメンド手法の提案

UniTag 온톨로지를 이용한 태그 기반 음악 추천 기법

본 논문에서는 소셜 음악 사이트에서 사용자들이 생성한 태그를 바탕으로 음악을 추천하는 기법을 제안한다. 협력적 태깅 시스템은 사용자가 직접 선정한 단어를 콘텐츠에 부여할 수 있도록 하므로, 사용자의 선호도를 구체적으로 파악할 수 있는 정보를 제공한다. 특히, 감정을 표현하는 감정 태그들은 음악 장르나 음악가와 같이 사실을 나타내는 사실 태그들과는 다르게 선호도를 훨씬 직접 표현하고 있다. 따라서 태그의 의미를 파악하여 감정 태그와 사실 태그로 분류하고, 감정 태그는 감정표현의 정도에 따라 가중치를 부여하기 위해서 UniTag라고 하는 태그 온톨로지를 개발하였다. UniTag 온톨로지를 이용하여 정제된 태그 집합은 사용자 프로파일 생성에 사용되며, 태그 기반 사용자 프로파일을 바탕으로 음악 추천 알고리즘을 수행하였다. 제안하는 추천 방법의 효율성을 평가하기 위해서, 전통적인 청취 횟수 기반 추천, 감정 태그 가중치를 고려하지 않은 추천, 그리고 감정 태그 가중치를 고려한 추천의 세 가지 추천 방법의 정확도와 재현율을 비교하였다. 실험 결과는, 감정 태그 가중치를 고려한 추천 방식이 정확도의 측면에서 다른 두 가지 방식보다 효율적이라는 것을 보여준다. 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.

From Popularity to Personality — A Heuristic Music Recommendation Method for Niche Market

In most current recommender systems, the goal to accurately predict what people want leads to the tendency to recommend popular items, which is less helpful in revealing user's personality, especially to new users. In this paper, we propose a heuristic music recommendation method for niche market by focusing on how to identify user's personality as soon as possible. Instead of trying to improve algorithm's performance on new users by recommending the most popular items, we work on how to make them "familiar" with the system earlier. The method is more suitable for brand-new users, and gives a hint to solve the cold start problem. In real applications it is better to combine it with a traditional approach.