Corpus ID: 33865288

A survey of music recommendation and possible improvements

  title={A survey of music recommendation and possible improvements},
  author={Jacob O’Bryant},
With the prevalence of the internet, mobile devices and commercial streaming music services, the amount of digital music available is greater than ever. Sorting through all this music is an extremely time-consuming task. Music recommendation systems search through this music automatically and suggest new songs to users. Music recommendation systems have been developed in commercial and academic settings, but more research is needed. The perfect system would handle all the user’s listening needs… Expand
1 Citations
Music Instrument Recognition using Machine Learning Algorithms
This work is based on designing an application that recognizes the instruments that are present in a given Music, and makes use of ANN (Artificial Neural Network) and CNN(Convolutional Neural Network). Expand


Music Recommendation Based on Acoustic Features and User Access Patterns
This paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns and significantly improves theMusic similarity measurement accuracy and performance. Expand
A Survey of Music Recommendation Systems and Future Perspectives
Along with the rapid expansion of digital music formats, managing and searching for songs has become significant. Though music information retrieval (MIR) techniques have been made successfully inExpand
Ameliorating Music Recommendation: Integrating Music Content, Music Context, and User Context for Improved Music Retrieval and Recommendation
This paper addresses user-centric models, which have been neglected for a long time in music retrieval and recommendation approaches, and addresses the following tasks: geospatial music recommendation from microblog data, user-aware music playlist generation on smart phones, and matching places of interest and music. Expand
Improving Music Recommendation in Session-Based Collaborative Filtering by Using Temporal Context
This work compared two techniques to capture the users' listening patterns over time and found that the inclusion of temporal information, either explicitly or implicitly, increases significantly the accuracy of the recommendation, while compared to the traditional session-based CF. Expand
Contextualize Your Listening: The Playlist as Recommendation Engine
A fully automatic interactive radio system is proposed, using audio-content and social network data as a backbone, and a novel distance metric between playlists is specified and tested. Expand
Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals
The results of an empirical evaluation show that although the short-term listening history should generally govern the next-track selection process, long-term preferences can measurably help to increase the personalization quality. Expand
NextOne Player: A Music Recommendation System Based on User Behavior
This work uses “Forgetting Curve” to assess freshness of a song and evaluate “favoredness” using user log, and analyzes user’s listening pattern to estimate the level of interest of the user in the next song. Expand
The neglected user in music information retrieval research
This article investigates and discusses literature on the topic of user-centric music retrieval and reflects on why the breakthrough in this field has not been achieved yet, and presents ideas on aspects crucial to consider when elaborating user-aware music retrieval systems. Expand
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
A new approach to music recommendation is presented by formulating this exploration-exploitation trade-off as a reinforcement learning task that uses a Bayesian model that accounts for both audio content and the novelty of recommendations. Expand
Session-Based Collaborative Filtering for Predicting the Next Song
This work proposes a simple but effective recommendation method for music recommender systems called Session-based Collaborative Filtering (SSCF), and looks into the different parameters that affect the recommendation accuracy. Expand