A personalized music recommendation system using convolutional neural networks approach

Chang, Shun-Hao; Abdul, Ashu; Chen, Jenhui; Liao, Hua-Yuan

doi:10.1109/icasi.2018.8394293

Cited by 40 publications

(13 citation statements)

References 5 publications

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“…Using convolutional neural networks (CNN) as the foundation, Chang et al (2018) offer the PMRS, or customized music recommendation system. The CNN method divides music into several genres based on the audio signal rhythms.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…The SNN gains the ability to function as a similarity metric between songs based on the raw audio information by being trained on this dataset of song picture pairs (Phaneendra et al, 2022). Using convolutional neural networks (CNN) as the foundation, Chang et al (2018) offer the PMRS, or customized music recommendation system. The CNN method divides music into several genres based on the audio signal rhythms.…”

Section: Introductionmentioning

confidence: 99%

“…They created a mobile application to demonstrate how the PMRS functions (an Android version). To evaluate the effectiveness of the PMRS, they used the confidence score metrics for various musical genres (Chang et al, 2018). By examining the history records of user tracks in the data sets, Wang et al (2018) technique RTCF (Tag-driven Collaborative Filtering Recommendation System) authors created a fair scoring system for users based on the training statistical language model (Good-Turing Estimate).…”

Section: Introductionmentioning

confidence: 99%

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Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

Faruk¹,

GHOSH²

2022

EPSTEM

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In the modern scenario, everyone uses the internet to find music, movies, products, services and other commodities on a regular basis to make their lives easier. Because of a lot of data on millions of music, movie, products and services on websites, we need a recommender system very much to assist people in making decisions more quickly and easily. In this research study, we have developed an intelligent music recommendation system by integrating a Music Genre Classification (MGC) with different types of Deep Learning Techniques such as RNN-LSTM, GRU and CNN. We have used the GTZAN Genre dataset to training our system. We have extracted the features from GTZAN dataset by the help of Mel Frequency Cepstral Coefficients (MFCCs) then pass the MFCCs into our deep learning networks. After classifying the appropriate music genre, recommended the music from particular genre from the labelled database which has been classified by our system. From our proposed models the GRU, CNN and RNN-LSTM produced about 71%, 72% and 74% respectively in our testing accuracy. The RNN-LSTM has achieved the best accuracy result (74%) among all of our proposed models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

Faruk¹,

GHOSH²

2022

EPSTEM

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“…The first is Deep Convolutional Neural Networks (DCNN) and the second is Weighted Feature Extraction (WEF). Like their previous paper [13] they have used DCNN to extract music latent features of the song such as the genre, the mood related data such as energy and the pace. The DCNN uses both the songs' meta-data and the audio to classify the song into different genres.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

SessNet: A Hybrid Session-Based Recommender System

Nada¹

2021

Preprint

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<div>Session-based recommendation is the task of predicting user actions during short online sessions. Previous work considers the user to be anonymous in this setting, with no past behavior history available. In reality, this is often not the case, and none of the existing approaches are flexible enough to seamlessly integrate user history when available. In this thesis, we propose a novel hybrid session-based recommender system to perform next-click prediction, which is able to take advantage of historical user preferences when accessible. Specifically, we propose SessNet, a deep profiling session-based recommender system, with a two-stage dichotomy. First, we use bidirectional transformers to model local and global session intent. Second, we concatenate any user information with the current session representation to feed to a feed-forward neural network to identify the next click. Historical user preferences are computed using the sequence-aware embeddings obtained from the first step, allowing us to better understand the users. We evaluate the efficacy of the proposed method using two benchmark datasets, YooChoose1/64 and Dignetica. Our experimental results show that SessNet outperforms state-of-the-art session-based recommenders on P@20 for both datasets.</div>

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