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Single and Ensemble Classification for Predicting User's Restaurant Preference

Esra'a Alshdaifat, Ala'a Al-shdaifat
2020 International Journal of Advanced Computer Science and Applications  
According to the reported experimental results, an effective Restaurant Category Preferences Prediction Model (RCPPM) could be generated using classification algorithms.  ...  Two main categories of classification algorithms can be adopted for generating prediction models: Single and Ensemble classification algorithms.  ...  Among these music categorization and movie genre predictions or genre preferences prediction [12] , [13] could be considered as entertainment applications of classification.  ... 
doi:10.14569/ijacsa.2020.0110782 fatcat:3h6hc4nbqrhjhburyaiuucpaiy

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  
In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences  ...  A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music  ...  Finally, the classification and prediction of user preference music are realized according to the relationship between user behavior and music preference.  ... 
doi:10.1155/2021/4953288 pmid:34868290 pmcid:PMC8642031 fatcat:3kk5jcsfjbfy7l2dvq7dmndcda

User Musical Taste Prediction Technique Using Music Metadata and Features

Minseo Gong, Jae-Yoon Cheon, Young-Suk Park, Jeawon Park, Jaehyun Choi
2016 International Journal of Multimedia and Ubiquitous Engineering  
In the stage of machine learning, we produce a prediction model in a variety of classification techniques.  ...  Therefore, this paper suggests technique to predict the user's musical taste.  ...  Random Forest algorithm was first proposed by Ho, et al. [17] . It demonstrated operation of the algorithm by "Law of large numbers".  ... 
doi:10.14257/ijmue.2016.11.8.18 fatcat:73dkz53x7fbg5k2dhaufrbin4y

Personalized Song Recommendation System Based on Vocal Characteristics

Jingzhou Yang, Naeem Jan
2022 Mathematical Problems in Engineering  
The song recommendation algorithm makes personalized recommendations by analyzing user's historical behavior, which can reduce user's information fatigue and improve the user experience.  ...  The characteristic of this work is to use the comprehensive features of spectrum and musical notes as the classification basis.  ...  Combined with the behavioral preference relationship between users and music, the prediction of user's preferred music classification is realized.  ... 
doi:10.1155/2022/3605728 fatcat:unw6b3fam5bsrkiijq5to5ypqy

Music preference learning with partial information

Yvonne Moh, Peter Orbanz, Joachim M. Buhmann
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
Specifically, we address the classification of music types according to a user's preferences for a hearing aid application. The classifier has to operate under limited computational resources.  ...  We propose an online learning algorithm capable of incorporating information from unlabeled data by a semi-supervised strategy, and demonstrate that the use of unlabeled examples significantly improves  ...  The authors gratefully acknowledge financial support by a grant from KTI.  ... 
doi:10.1109/icassp.2008.4518036 dblp:conf/icassp/MohOB08 fatcat:yhcpiwfc4fax3dzmtkf5prhnwe

Effective prediction on music therapy using hybrid SVM-ANN approach

K Devendran, S K Thangarasu, P Keerthika, R Manjula Devi, B K Ponnarasee, J. Kannan R., P. Kommers, A. S, A. Quadir Md
2021 ITM Web of Conferences  
Here we aimed to establish the main predictive factors of music listening's relaxation and the prediction of music for music therapy using various machine learning algorithms such as Decision tree, Random  ...  prediction.  ...  Now the dataset is fed into various machine learning algorithm for classification and prediction of music.  ... 
doi:10.1051/itmconf/20213701014 fatcat:k5do62h2bfab7m45r6ffd5c5p4

Ensemble Learning For Hybrid Music Recommendation

Marco Tiemann, Steffen Pauws, Fabio Vignoli
2007 Zenodo  
INTRODUCTION Music recommender systems that are publicly available today apply one of two recommender paradigms: social recommenders predict preferences based purely on user preference data.  ...  This method has been shown to perform well for different classification tasks [2] . For the regression task of predicting preference values, we apply a variant of this method.  ... 
doi:10.5281/zenodo.1417780 fatcat:ijiv25fb2befpgue577s5dnty4

Towards ensemble learning for hybrid music recommendation

Marco Tiemann, Steffen Pauws
2007 Proceedings of the 2007 ACM conference on Recommender systems - RecSys '07  
We investigate ensemble learning methods for hybrid music recommender algorithms, combining a social and a contentbased recommender algorithm as weak learners by applying a combination rule to unify the  ...  A first experiment suggests that such a combination can already reduce the mean absolute prediction error compared to the weak learners' individual errors.  ...  Social recommenders predict preferences based purely on user preference data.  ... 
doi:10.1145/1297231.1297265 dblp:conf/recsys/TiemannP07 fatcat:2whpdhgnjzbkhkqoefexutwnoq

Using Deep learning methods for generation of a personalized list of shuffled songs [article]

Rushin Gindra, Asmita Natekar (1 and 2) and Grishma Sharma K. J. Somaiya College of Engineering, Assistant Professor, Project Adviser)
2019 arXiv   pre-print
These categories will be shuffled retrospectively based on the metadata to autonomously provide with a list that is efficacious in playing songs that are desired by humans in normal conditions.  ...  There are only few music enthusiasts who use this mode since it either is too random to suit their mood or it keeps on repeating the same list every time.  ...  The effectiveness of the algorithm shall be obtained by analyzing individual user's music collection and their feedback on how does the algorithm help them organize it based on their preferences.  ... 
arXiv:1712.06076v2 fatcat:w3rvqlw3jbaknpjj4qombp2liy

GENRE PREDICTION FOR MUSIC RECOMMENDATION USING MACHINE LEARNING

Arpit Seth
2020 EPRA international journal of research & development  
The flow of this paper is to increase the efficiency of music recommendation in terms of the genre based on the decision-tree which helps the users to get the music according to their preferences.  ...  The model will predict the genre according to age and gender and the decision tree helps to reduce the complexity of the model.  ...  Unlike other supervised learning algorithms, algorithms for decision tree can also be used to solve regression and classification problems.  ... 
doi:10.36713/epra4283 fatcat:oczykynz25fwnccuecpdqro5ji

CD-CARS: Cross-Domain Context-Aware Recommender Systems

Douglas Véras, Ricardo Prudêncio, Carlos Ferraz
2019 Expert systems with applications  
is higher than one method that predicts user preference by merging the user ratings from all domains.  ...  The algorithms were evaluated regarding their predictive and classification performances, which were analyzed by means of statistical significance tests.  ... 
doi:10.1016/j.eswa.2019.06.020 fatcat:zckz6fjqnrcfnebrbtm5bme534

Genre-based Link Prediction in Bipartite Graph for Music Recommendation

Daozhen Zhao, Lingling Zhang, Weiqi Zhao
2016 Procedia Computer Science  
However, that links cannot represent the users' dual preferences (like and dislike).  ...  In experiments with the Xiami.com music dataset, the proposed music genre weight-based music recommendation model (MGW) performances better than the CORLP method.  ...  This work was partially supported by National Natural Science Foundation of China  ... 
doi:10.1016/j.procs.2016.07.121 fatcat:2rjmgpcc6rbyfjaecmlas5k5fq

Music Recommendation System and Recommendation Model Based on Convolutional Neural Network

Yezi Zhang, Chia-Huei Wu
2022 Mobile Information Systems  
The classification proposal achieves its goal by denying the similarity between customer preferences and the potential of two musical characteristics.  ...  the classification results and design recommendation algorithms.  ...  Music recommendation algorithms predict and push user behavioral preferences based on user behavioral information and music characteristics.  ... 
doi:10.1155/2022/3387598 fatcat:asa4bt6j2naxfnuxihkhdp6hdq

Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres

Aleksandra Dorochowicz, Adam Kurowski, Bożena Kostek
2020 Electronics  
., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms  ...  The prediction of a personality type was performed employing four baseline algorithms, i.e., support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF), and naïve Bayes (NB).  ...  Predictive accuracies obtained by Nave et al.  ... 
doi:10.3390/electronics9122016 fatcat:em4n2xmvhjhbbevytxuw42hzym

Tempo and beat tracking for audio signals with music genre classification

Mao Yuan Kao, Chang Biau Yang, Shyue Horng Shiau
2009 International Journal of Intelligent Information and Database Systems  
And then, with the preference classification, we can obtain accurate estimation for tempo and beats, by either Ellis's method or Dixon's method.  ...  Most people follow the music to hum or the rhythm to tap sometimes. We may get different meanings of a music style if it is explained or felt by different people.  ...  Acknowledgements This research work was partially supported by the National Science Council of Taiwan under contract NSC 95-2745-H-309-003-HPU.  ... 
doi:10.1504/ijiids.2009.027687 fatcat:zpm3tgooobdlxamey52wgn3msa
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