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Musical recommendations and personalization in a social network

Dmitry Bugaychenko, Alexandr Dzuba
2013 Proceedings of the 7th ACM conference on Recommender systems - RecSys '13  
This paper presents a set of algorithms used for music recommendations and personalization in a general purpose social network www.ok.ru, the second largest social network in the CIS visited by more then  ...  In addition to classical recommendation features like "recommend a sequence" and "find similar items" the paper describes novel algorithms for construction of context aware recommendations, personalization  ...  All data mined from the history of users' activity, content metadata and social network are combined in a taste graph.  ... 
doi:10.1145/2507157.2507192 dblp:conf/recsys/BugaychenkoD13 fatcat:murrf6e63rcmjovupe3yjoeub4

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.  ...  playback coefficient.  ... 
doi:10.1155/2021/3140341 fatcat:ifu7tintrbfg3dougzci6woaei

Songrium: Browsing And Listening Environment For Music Content Creation Community

Masahiro Hamasaki, Masataka Goto, Tomoyasu Nakano
2015 Proceedings of the SMC Conferences  
Acknowledgments We thank Keisuke Ishida for the web service implementation of Songrium. We also thank anonymous users of Songrium for editing social annotations.  ...  Furthermore, Songrium3D shows automatically synthesized visual effects for each usergenerated music content during music playback.  ...  Regarding this point, visualizing massive user-generated music content can provide an excellent experience for active listeners, having a complementary relation with music recommendation.  ... 
doi:10.5281/zenodo.851090 fatcat:iwcwy7widzgyxcfyooh2ndafim

Engaging with Mobile Music Retrieval

Daniel Boland, Ross McLachlan, Roderick Murray-Smith
2015 Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services - MobileHCI '15  
The large music collections available to users today can be overwhelming in mobile settings, they offer 'too-much-choice' to users, who often resort to shufflebased playback.  ...  We develop a series of metrics to capture music listening behaviour from users' interaction logs.  ...  ACKNOWLEDGMENTS This work was supported by Bang & Olufsen, the Danish Council for Strategic Research of the Danish Agency for Science Technology and Innovation under the CoSound project, case number 11  ... 
doi:10.1145/2785830.2785846 dblp:conf/mhci/BolandMM15 fatcat:264ixxchmvazpc32vrwvp3xuvy

Intelligent User Interfaces for Music Discovery: The Past 20 Years and What's to Come

Peter Knees, Markus Schedl, Masataka Goto
2019 Zenodo  
Therefore, already the first edition of ISMIR in the year 2000 called for papers addressing the topic of "User interfaces for music IR".  ...  In this paper, we reflect on the evolution of MIR-driven user interfaces for music browsing and discovery over the past two decades.  ...  Music recommendation typically models personal preferences of users by using their listening histories or explicit user feedback, e.g. [7, 62] .  ... 
doi:10.5281/zenodo.3527737 fatcat:kaj6hodbfjdyhodl5avk2ciloq

Mobile Music Genius

Markus Schedl, Georg Breitschopf, Bogdan Ionescu
2014 Proceedings of International Conference on Multimedia Retrieval - ICMR '14  
The amount of music consumed while on the move has been spiraling during the past couple of years, which requests for intelligent music recommendation techniques.  ...  In this demo paper, we introduce a context-aware mobile music player named "Mobile Music Genius" (MMG), which seamlessly adapts the music playlist on the fly, according to the user context.  ...  Baltrunas et al. suggest a context-aware music recommender for listening while driving [1] .  ... 
doi:10.1145/2578726.2582612 dblp:conf/mir/SchedlBI14 fatcat:jqfwpe7v2fcr7f2m2oh6sbtbl4

Wi-Fi Walkman [chapter]

Marcel Reinders, Jun Wang, A de Vries
2007 Encyclopedia of Wireless and Mobile Communications  
It features audio playback, audio storage, audio recommendation, and ad hoc wireless connectivity for audio exchange.  ...  Create user interest Music recommendation server Filtering User's play -list Peer-to-peer network Peer/music finding Select Downloading/ streaming Playback 1 2 3 4 5 6 7  ... 
doi:10.1201/noe1420043266.ch137 fatcat:ot7h5bo5lndtbhh7q5s5hayx4y

Improving the Quality of the Personalized Electronic Program Guide

Derry O'Sullivan, Barry Smyth, David C. Wilson, Kieran McDonald, Alan Smeaton
2004 User modeling and user-adapted interaction  
We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovery by mining ratings-based  ...  of individual users in order to compile personalized viewing guides that fit their individual preferences.  ...  For example, each Play profile contains all programmes selected for playback by an particular user even though some of these playback actions may be unreliable preference indicators.  ... 
doi:10.1023/b:user.0000010131.72217.12 fatcat:4mfckd5tyjhtfb7rnfrrgxfhg4

Music Personalized Label Clustering and Recommendation Visualization

Yongkang Huo, Wei Wang
2021 Complexity  
Based on these algorithms and based on the user history data information and music data information that can be found now, the paper aims to build a personalized music recommendation system based on directed  ...  tags, which can provide basic music services to users and push them personalized music recommendation lists.  ...  Detailed requirement analysis and design of each function in the personalized music recommendation system are carried out, and functions such as music playback control, music list, music retrieval, user  ... 
doi:10.1155/2021/5513355 fatcat:xulzh43e5na6tcpejclnkdxk6i

User-Influenced/Machine-Controlled Playback: The variPlay Music App Format for Interactive Recorded Music

Justin Paterson, Rob Toulson, Russ Hepworth-Sawyer
2019 Arts  
Opportunities for future development are also presented.  ...  This project drew from three consecutive rounds of research funding to develop an app format that could host both user interactivity to change the sound of recorded music in real-time, and a machine-driven  ...  Data Mining A major benefit of variPlay is that it allows anonymous data mining of listener behavior in ways not captured by other download or streaming music platforms.  ... 
doi:10.3390/arts8030112 fatcat:qs6rtt53gzh4pntrb2amn7sa7i

Personalization in Multimodal Music Retrieval [chapter]

Markus Schedl, Peter Knees
2013 Lecture Notes in Computer Science  
In this vein, this contribution aims at defining the foundation for future research directions and applications related to multimodal music information systems.  ...  The first one is taking into account the music context of a piece of music or an artist, while the second aspect tackled is that of the user context.  ...  Another source for the music context is collaborative tags, mined for example from last.fm [32] in [12, 36] or gathered via tagging games [41, 74, 34] .  ... 
doi:10.1007/978-3-642-37425-8_5 fatcat:ikpeobohjvcjbcqf6jbzt4uisu

From A Music Industry To Sound Industries

Thor Magnusson
2013 Zenodo  
Commodification has been an inherent aspect of music for many centuries.  ...  New technologies, media formats, and practices appear regularly, requiring swift responses by the incumbent music industry.  ...  of human history.  ... 
doi:10.5281/zenodo.437689 fatcat:upmnlinxbbfmvhbhv4heuyxbbu

Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution [article]

Arash Mahyari, Peter Pirolli, Jacqueline A. LeBlanc
2022 arXiv   pre-print
In this paper, we develop a recommendation system for daily exercise activities to users based on their history, profile and similar users.  ...  The active learners calculate the uncertainty of the recommender at each time step for each user and ask an expert for a recommendation when the certainty is low.  ...  Thus, these streaming service providers deploy recommendation systems to suggest new movies and musics to their existing and new users based on their history of watching movies and listening to musics  ... 
arXiv:2110.06287v2 fatcat:sjptxzsbbffirmx4nbdmu52fv4

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Gao Yang, Muhammad Arif
2022 Security and Communication Networks  
In terms of recommendation methods, the music-music recommendation method based on predicting user behavior data and the recommendation method based on automatic tag generation are proposed.  ...  This research aims to create a better music algorithm that incorporates user data for deep learning, a candidate matrix compression technique for suggestion improvement, accuracy, recall rate, and other  ...  However, because of the huge amount of music, it does not support retrieval and playback. erefore, the system supports randomly generating a list of users' favorite music directly for users and using this  ... 
doi:10.1155/2022/7696840 fatcat:kvebk7hgbndxfjpwvzl5hr4mue

Research on TikTok APP Based on User-Centric Theory

Jiang Xiao Yu
2019 Applied Science and Innovative Research  
recommended algorithm technology based on big data, which enhanced user loyalty.  ...  <p>TikTok App, as a music short video social platform, has become a popular style in short video field in 2018, dues to its huge user base and vast amount of content.  ...  TikTok's core technologies are text mining, machine data mining, personalized recommendation engines, and more.  ... 
doi:10.22158/asir.v3n1p28 fatcat:z3lcwagnnnb3xpsu4kkbaxpzwm
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