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Design and user issues in personality-based recommender systems

Rong Hu
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
In this paper, I present my up-to-date results on a proposed personality-based music recommender prototype, user perception investigations, and my ongoing research about addressing new user problem by  ...  The utilization of personality characteristics into recommender systems and the exploration of user perception to such systems are the focuses of my thesis.  ...  RESEARCH WORK UP-TO-DATE 3.1 Personality-based Music Recommender Prototype According to the work of Rentfrow [19] , we proposed a framework for personality-based music recommender systems [8] .  ... 
doi:10.1145/1864708.1864790 dblp:conf/recsys/Hu10 fatcat:5awgvbavs5btlniojc25zwye6u

A Web Recommender System for Recommending, Predicting and Personalizing Music Playlists [chapter]

Zeina Chedrawy, Syed Sibte Raza Abidi
2009 Lecture Notes in Computer Science  
In this paper, we present a Web recommender system for recommending, predicting and personalizing music playlists based on a user model.  ...  Our Web recommender system features three functionalities: (1) predict the likability of a user towards a specific music playlist, (2) recommend a set of music playlists, and (3) compose a new personalized  ...  In this paper, we present a Web recommender system for recommending, predicting and personalizing music playlists based on a user model.  ... 
doi:10.1007/978-3-642-04409-0_34 fatcat:z7udxvd6tvhc5eh7uxd4exl5ne

Emotion Based Music Recommendation System

Mikhail Rumiantcev, Oleksiy Khriyenko
2020 Zenodo  
Recommendation systems gain more and more popularity and help people to select appropriate music for all occasions.  ...  This paper will present design of the personalized music recommendation system, driven by listener feelings, emotions and activity contexts.  ...  Further, based on the GUP system builds an initial Music-driven Emotional Model (MEM) of a user.  ... 
doi:10.5281/zenodo.4007449 fatcat:4hnpkero5vhbfj4hrprrlh7mpi

A Study on User Perception of Personality-Based Recommender Systems [chapter]

Rong Hu, Pearl Pu
2010 Lecture Notes in Computer Science  
Finally, we propose some design issues for recommender systems using personality quizzes.  ...  Our in-depth user studies show that while active users perceive the recommended items to be more accurate for their friends, they enjoy more using personality quiz based recommenders for finding items  ...  We thank the EPFL and Chinese Government for sponsoring the reported research work. We are grateful to the participants for their patience and time.  ... 
doi:10.1007/978-3-642-13470-8_27 fatcat:uny42pg6vrckbgwmmzrmknnq24

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Hui Ning, Qian Li, Wei Wang
2020 Complexity  
On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction  ...  for users, introduces the concept of popularity.  ...  It studies the personalized personalized music recommendation problem for personalized music recommendation users. is article combines content-based recommendation technology, popularity multiple linear  ... 
doi:10.1155/2020/6643888 fatcat:cqfikrqo7nehneqztobifq5x3i

Adaptive user preference modeling and its application to in-flight entertainment

Hao Liu, Ben Salem, Matthias Rauterberg
2008 Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts - DIMEA '08  
This paper first presents an adaptive user preference model for personalized service delivery systems.  ...  Finally, we customized the user preference for personalized in-flight entertainment recommendation to validate its features.  ...  Architecture of an Adaptive In-flight Entertainment System Music Selection Algorithm Based on User Preference The adaptive in-flight entertainment system recommends personalized music to the passenger  ... 
doi:10.1145/1413634.1413688 dblp:conf/dimea/LiuSR08 fatcat:hjov762xwna4hhfe2uod4w7h2u

Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network

Yuanyuan Zhang, Bai Yuan Ding
2021 Computational Intelligence and Neuroscience  
The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on.  ...  piano music recommendation for users in the big data environment.  ...  the piano music recommendation system based on the CNN and expounds the personalized recommendation system under big data and the recommendation algorithm based on deep learning.  ... 
doi:10.1155/2021/4953288 pmid:34868290 pmcid:PMC8642031 fatcat:3kk5jcsfjbfy7l2dvq7dmndcda

Using Factor Decomposition Machine Learning Method to Music Recommendation

Dapeng Sun, Zhihan Lv
2021 Complexity  
The idea of user-based collaborative filtering (CF) is used for reference to obtain music works favored by similar users.  ...  On the other hand, we learn from item-based CF, which ensures that the candidate set covers user preference. Firstly, the user's interest value is predicted by using dynamic interest model.  ...  Conflicts of Interest e authors declare that they have no known conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1155/2021/9913727 fatcat:6diknz2k5famlgaigurtcam2tu

Personalized Analysis and Recommendation of Aesthetic Evaluation Index of Dance Music Based on Intelligent Algorithm

Jun Geng, Muhammad Javaid
2021 Complexity  
The emergence of dance music recommendation system can recommend dance music that users may like and help users quickly discover or find their favorite dances and songs.  ...  According to different groups of individual aesthetic standards of dance music, this paper introduces the idea of relation learning into dance music recommendation system and applies the relation model  ...  For users, the personalized content provided by the recommendation system will improve the user experience, thus saving them a lot of time.  ... 
doi:10.1155/2021/1026341 fatcat:opulbwzlkvhbfnjxvrq6okfpeq

Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Meilin Lu, Fangfang Deng, Chi-Hua Chen
2021 Security and Communication Networks  
Personalized music recommendations can accurately push the music of interest from a massive song library based on user information when the user's listening needs are blurred.  ...  To this end, this paper proposes a method of national music recommendation based on ontology modeling and context awareness to explore the use of music resources to portray user preferences better.  ...  A personalized recommendation system is proposed to solve the common needs of enterprises and users and user preferences. is recommendation system can collect users' behavioral preferences, personal attributes  ... 
doi:10.1155/2021/3140341 fatcat:ifu7tintrbfg3dougzci6woaei

A Comprehensive Survey of Personalized Music Identifier System

Soumi Ghosh, Devanshu Tyagi, Daksh Vashisht, Abhishek Yadav, Dharmendra Rajput
2021 International journal of recent technology and engineering  
for music identification or recommendation.  ...  In future, more in-depth studies or research work need to be conducted based on enlarging the scope of further development of personalized contextual awareness based music identifier system and generating  ...  proposed very many models or approaches for a successful music recommendation system.  ... 
doi:10.35940/ijrte.f5316.039621 fatcat:mspco6bxsvhddolhebdui5jtte

Preface to the Special Issue on user modeling for personalized interaction with music

Marko Tkalčič, Markus Schedl, Peter Knees
2020 User modeling and user-adapted interaction  
for music recommender systems.  ...  systems for music.  ... 
doi:10.1007/s11257-020-09264-6 fatcat:fsrgzag6tbfwjcvngbhu4ormye

LsM: A new location and emotion aware web-based interactive music system

Hao Liu, Jun Hu, M. Rauterberg
2010 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE)  
Also, the more the user uses the system, the more personalized music can be adapted to him/her.  ...  The system starts recommendation with expert knowledge. If the user does not like the recommendation, he/she can decline the recommendation and select the desired music himself/herself.  ...  The user feedback log component is responsible for logging the user's feedback to the recommended music.  ... 
doi:10.1109/icce.2010.5418750 fatcat:rz7c7oyysncohbi7pc7ngafqve

Music Exploration by Impression Based Interaction

Hiroki Fujino, Koiti Hasida, Yusuke Matsubara
2017 Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics - ESIDA '17  
Nevertheless, it is still hard for us to efficiently retrieve the right music.  ...  Search and recommendation systems help users find their favorites among an abundant amount of songs available on distributors such as iTunes.  ...  Music Retrieval In the part of music retrieval by the query, our system recommends songs based on the user model and features of music described below.  ... 
doi:10.1145/3038462.3038468 dblp:conf/iui/FujinoHM17 fatcat:kjp7gt4uenbtpjrrnj3tzyjzvq

From Sensors to Songs: A Learning-Free Novel Music Recommendation System using Contextual Sensor Data

Abhishek Sen, Martha A. Larson
2015 ACM Conference on Recommender Systems  
The goal for our proposed system is to provide novel music recommendations based on contextual sensor information.  ...  Traditional approaches for music recommender systems face the known challenges of providing new recommendations that users perceive as novel and serendipitous discoveries.  ...  Through inference of user preferences based on collection-wide user experiences of context, we think the system will achieve a level of personalization that is ideal for music recommender systems-without  ... 
dblp:conf/recsys/SenL15 fatcat:zgqyhwhfdjf5jplzcl3424cpiq
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