Papers Music Recommendation
“Music Recommendation” 태그가 달린 논문 118편 · 필터 해제
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Recent works of music representation learning mainly focus on learning acoustic music representations with unlabeled audios or further attempt to acquire multi-modal music representations with scarce annotated audio-text…
Collaborative FilteringContrastive LearningMusic RecommendationRepresentation LearningGraphs are everywhere -- Psst! In Music Recommendation too
In recent years, graphs have gained prominence across various domains, especially in recommendation systems. Within the realm of music recommendation, graphs play a crucial role in enhancing genre-based recommendations b…
Collaborative FilteringMusic RecommendationRecommendation SystemsText2Tracks: Prompt-based Music Recommendation via Generative Retrieval
In recent years, Large Language Models (LLMs) have enabled users to provide highly specific music recommendation requests using natural language prompts (e.g. "Can you recommend some old classics for slow dancing?"). In …
Entity ResolutionMusic RecommendationRetrievalEmotion Detection and Music Recommendation System
As artificial intelligence becomes more and more ingrained in daily life, we present a novel system that uses deep learning for music recommendation and emotion-based detection. Through the use of facial recognition and …
Music RecommendationTALKPLAY: Multimodal Music Recommendation with Large Language Models
We present TALKPLAY, a novel multimodal music recommendation system that reformulates recommendation as a token generation problem using large language models (LLMs). By leveraging the instruction-following and natural l…
Conversational RecommendationInstruction FollowingLanguage ModelingLanguage Modelling+5LIBER: Lifelong User Behavior Modeling Based on Large Language Models
CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While leveraging semantic information from LLMs …
Click-Through Rate PredictionMusic RecommendationRecommendation SystemsAttention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification
Music genre classification is a critical component of music recommendation systems, generation algorithms, and cultural analytics. In this work, we present an innovative model for classifying music genres using attention…
ClassificationGenre classificationMusic Genre ClassificationMusic Recommendation+1Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation
Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categories or consumption contexts, from trai…
Information RetrievalMusic RecommendationNavigateRecommendation Systems+1MIRFLEX: Music Information Retrieval Feature Library for Extraction
This paper introduces an extendable modular system that compiles a range of music feature extraction models to aid music information retrieval research. The features include musical elements like key, downbeats, and genr…
BenchmarkingInformation RetrievalInstrument RecognitionMusic Information Retrieval+2Arabic Music Classification and Generation using Deep Learning
This paper proposes a machine learning approach for classifying classical and new Egyptian music by composer and generating new similar music. The proposed system utilizes a convolutional neural network (CNN) for classif…
ClassificationDeep LearningMusic ClassificationMusic Recommendation+1Audio Processing using Pattern Recognition for Music Genre Classification
This project explores the application of machine learning techniques for music genre classification using the GTZAN dataset, which contains 100 audio files per genre. Motivated by the growing demand for personalized musi…
Genre classificationMusic Genre ClassificationMusic RecommendationRecommendation SystemsTowards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks
Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable insights for predictions, new music piece…
Collaborative FilteringMusic RecommendationRecommendation SystemsComparative Analysis of Pretrained Audio Representations in Music Recommender Systems
Over the years, Music Information Retrieval (MIR) has proposed various models pretrained on large amounts of music data. Transfer learning showcases the proven effectiveness of pretrained backend models with a broad spec…
Genre classificationInformation RetrievalMusic Information RetrievalMusic Recommendation+2Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation -- continuously providing new items within a single session in a contextually coherent mann…
Contrastive LearningMusic RecommendationSequential RecommendationSession-Based RecommendationsMamba for Scalable and Efficient Personalized Recommendations
In this effort, we propose using the Mamba for handling tabular data in personalized recommendation systems. We present the \textit{FT-Mamba} (Feature Tokenizer\,$+$\,Mamba), a novel hybrid model that replaces Transforme…
Computational EfficiencyMambaMusic RecommendationRecommendation Systems+1Benchmarking Sub-Genre Classification For Mainstage Dance Music
Music classification, with a wide range of applications, is one of the most prominent tasks in music information retrieval. To address the absence of comprehensive datasets and high-performing methods in the classificati…
BenchmarkingClassificationGenre classificationInformation Retrieval+3Enhancing Sequential Music Recommendation with Personalized Popularity Awareness
In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption. Nevertheless, traditional Transformer-based models, such as SASRec and BERT4Re…
Music RecommendationRecommendation SystemsSequential RecommendationTransformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
Music streaming services often leverage sequential recommender systems to predict the best music to showcase to users based on past sequences of listening sessions. Nonetheless, most sequential recommendation methods ign…
Music RecommendationRecommendation SystemsSequential RecommendationIt's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in music recommendation is artist gender fairne…
FairnessMusic RecommendationRecommendation SystemsRe-RankingOh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems
Recent work suggests that music recommender systems are prone to disproportionally frequent recommendations of music from countries more prominently represented in the training data, notably the US. However, it remains u…
Music RecommendationRecommendation Systems