Effective social content-based collaborative filtering for music recommendation (original) (raw)
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Hybrid Approach to Music Recommender Systems
A large number of internet and consumer services applications involve predicting user responses to choices. Some examples include media recommendation systems implemented by Netflix \cite{Netflix} and Spotify \cite{Spotify}. Traditionally, recommender systems have been broadly classified into two categories: Collaborative Filtering and Content-Based Recommendation. We present a novel approach to recommend songs. The goal of the system is to recommend a song that is similar to the user's previously heard songs and is also rated highly by other similar user. We achieve this by using both community ratings and the metadata of songs, thus combining the traditional collaborative filtering and content-based recommendation approaches. We train a deep neural-network to predict ratings for the song and then make recommendations.
Expert Systems with Applications, 2016
The great quantity of music content available online has increased interest in music recommender systems. However, some important problems must be addressed in order to give reliable recommendations. Many approaches have been proposed to deal with cold-start and first-rater drawbacks; however, the problem of generating recommendations for gray-sheep users has been less studied. Most of the methods that address this problem are content-based, hence they require item information that is not always available. Another significant drawback is the difficulty in obtaining explicit feedback from users, necessary for inducing recommendation models, which causes the well-known sparsity problem. In this work, a recommendation method based on playing coefficients is proposed for addressing the above-mentioned shortcomings of recommender systems when little information is available. The results prove that this proposal outperforms other collaborative filtering methods, including those that make use of user attributes.
Music Recommendation System using Content and Collaborative Filtering Methods
Indian Scientific Journal Of Research In Engineering And Management, 2022
Rapid development of mobile devices and internet has made possible for us to access different music resources freely. While the Music industry may favor certain forms of music over others, it's important to grasp that there isn't one human culture on earth that has existed without music. during this paper, we've designed, implemented and analyzed a song recommendation system. we've used Song Dataset provided to search out correlations between users and songs and to find out from the previous listening history of users to supply recommendations for songs which users would like to concentrate most. The dataset contains over ten thousand songs and listeners are recommended the simplest available songs supported the mood, genre, artist and top charts of that year. With an interactive UI we show the listener the highest songs that were played the foremost and top charts of the year. Listener even have the choice to pick out his/her favorite artist and genres on which songs are recommended to them using the dataset.
Content and Popularity-Based Music Recommendation System
International Journal of Information System Modeling and Design
The future of many modern technologies includes machine learning and deep learning methodologies. One of the prominent applications of these technologies is the recommender system. Due to the rapid growth of the songs in digital formats, the searching and managing of songs has become a great problem. In this study, the authors developed a recommender system using popularity and rhythm content of the song. The studies compared various techniques to improve the robustness and minimal error of the system. The authors will mostly focus on content-based, popularity-based, and collaborative-based filtering algorithms and also try to combine them using a hybrid approach. The authors utilized MAE for comparing the several procedures implemented here for the recommendation. Out of all procedures used, SVD performed well with MAE of 1.60 while KNN didn't perform that well as the authors had fewer features of song with mean absolute error of 2.212. User-relied and item-relied prototypes pe...
IJERT-Music Recommendation System using Content and Collaborative Filtering Methods
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/music-recommendation-system-using-content-and-collaborative-filtering-methods https://www.ijert.org/research/music-recommendation-system-using-content-and-collaborative-filtering-methods-IJERTV10IS020071.pdf Rapid development of mobile devices and internet has made possible for us to access different music resources freely. While the Music industry may favor certain types of music more than others, it is important to understand that there isn't a single human culture on earth that has existed without music. In this paper, we have designed, implemented and analyzed a song recommendation system. We have used Song Dataset provided to find correlations between users and songs and to learn from the previous listening history of users to provide recommendations for songs which users would prefer to listen most. The dataset contains over ten thousand songs and listeners are recommended the best available songs based on the mood, genre, artist and top charts of that year. With an interactive UI we show the listener the top songs that were played the most and top charts of the year. Listener also have the option to select his/her favorite artist and genres on which songs are recommended to them using the dataset.
Enhancing collaborative filtering in music recommender system by using context based approach
Recommender Systems analyse some user and item interactions to help users by recommending the most relevant, feasible and appropriate items from a wide range and pool of items and resources. We propose to enhance the collaborative filtering methodology in recommender systems by considering the content as well as the context based approach towards recommendation. The rating of items and the contextual information of users are expected to enhance the relevance constraint by taking into account the user's mood and activity implicitly by using respective API's to capture and consider the contextual features of the user. The methodology and technique of reduction based approach and user based rating prediction will be used to accomplish the desired results for the proposed recommender system.
Myusic: a Content-based Music Recommender System based on eVSM and Social Media
This paper presents Myusic, a platform that leverages social media to produce content-based music recommendations. The design of the platform is based on the insight that user preferences in music can be extracted by mining Facebook profiles, thus providing a novel and effective way to sift in large music databases and overcome the cold-start problem as well. The content-based recommendation model implemented in Myusic is eVSM [4], an enhanced version of the vector space model based on distributional models, Random Indexing and Quantum Negation. The effectiveness of the platform is evaluated through a preliminary user study performed on a sample of 50 persons. The results showed that 74% of users actually prefer recommendations computed by social mediabased profiles with respect to those computed by a simple heuristic based on the popularity of artists, and confirmed the usefulness of performing user studies because of the different outcomes they can provide with respect to offline experiments.
A Probabilistic Model for Music Recommendation Considering Audio Features
Lecture Notes in Computer Science, 2005
In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focused on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to directly extract and utilize information from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. By utilizing audio features, this model provides a way to alleviate three well-known challenges in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experiments on a real-world data set illustrate that the audio information of music is quite useful and our system is feasible to integrate it for better personalized recommendation.