T-RECSYS: A Novel Music Recommendation System Using Deep Learning

Fessahaye, Ferdos; Pérez, Luís G.; Zhan, Tiffany; Zhang, Raymond; Fossier, Calais; Markarian, Robyn; Chiu, Carter; Zhan, Justin; Gewali, Laxmi; Oh, Paul

doi:10.1109/icce.2019.8662028

Cited by 63 publications

(20 citation statements)

References 76 publications

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“…Online music streaming platforms like Spotify, YouTube music, apple music rely on AI and DL for the music suggestion to their users. Still, novel ways to do the same task arises like T-RECSYS [103]. Services like Google, shazam used DL to extract music information from the audio [104].…”

Section: In Entertainmentmentioning

confidence: 99%

Deep Learning Application Pros And Cons Over Algorithm

Moshayedi

Roy

Kolahdooz

et al. 2022

EAI Endorsed Trans AI Robotics

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Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

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Section: In Entertainmentmentioning

confidence: 99%

Deep Learning Application Pros And Cons Over Algorithm

Moshayedi

Roy

Kolahdooz

et al. 2022

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

show abstract

“…Deep graph neural network (Yin et al , 2019), deep learning-based recommender system (Aujla et al , 2019), social attentive deep learning (Lei et al , 2020) and patient diet recommender system (Iwendi et al , 2020) were the key contributions in the field of RS. LSTM-based approaches (Zarzour et al , 2020), music recommender using deep learning (Fessahaye et al , 2019), autoencoder for top-N recommendations (Pan et al , 2019; Jiang et al , 2020; https://nijianmo.github.i; Zhu et al , 2017), reinforcement learning (Yuan et al , 2020), medical image segmentation using deep learning (Wang et al , 2020), topic-driven hybrid approach (Khan et al , 2020) and a deep learning approach with K pickup points (Berdeddouch et al , 2020) are important approaches found. Collaborative deep learning (Yang et al , 2020) and joint representation learning (Wu et al , 2017) are other approaches studied.…”

Section: Related Workmentioning

confidence: 99%

A multi-preference integrated algorithm (MPIA) for the deep learning-based recommender framework (DLRF)

Vikram

Reddy

Madhavi

2022

IJICC

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PurposeThe deep learning-based recommender framework (DLRF) is based on an improved long short-term memory (LSTM) structure with additional controllers; thus, it considers contextual information for state transition. It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences. An algorithm named a multi-preference integrated algorithm (MPIA) is proposed to have dynamic integration of both kinds of user preferences aforementioned. Extensive experiments are made using Amazon benchmark datasets, and the results are compared with many existing recommender systems (RSs).Design/methodology/approachRSs produce quality information filtering to the users based on their preferences. In the contemporary era, online RSs-based collaborative filtering (CF) techniques are widely used to model long-term preferences of users. With deep learning models, such as recurrent neural networks (RNNs), it became viable to model short-term preferences of users. In the existing RSs, there is a lack of dynamic integration of both long- and short-term preferences. In this paper, the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well.FindingsThe results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve (AUC) and F1-score. The percentage of improvement in terms AUC is observed as 1.3, 2.8, 3 and 1.9% and in terms of F-1 score 0.98, 2.91, 2 and 2.01% on the datasets.Originality/valueThe algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.

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“…On the other hand, recommendation systems have been used in e-commerce and online shopping for several years [18]- [21]. The goal of recommendation systems is to recommend products that suit the consumers' tastes.…”

Section: A Related Workmentioning

confidence: 99%

Real-Time Learning from an Expert in Deep Recommendation Systems with Application to mHealth for Physical Exercises

Mahyari

Pirolli

LeBlanc³

2022

IEEE J. Biomed. Health Inform.

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Recommendation systems play an important role in today's digital world. They have found applications in various areas such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of several diseases as well as healthcare costs. In this paper, we develop a recommendation system to recommend daily exercise activities to users based on their history, profiles and similar users. The developed recommendation system uses a deep recurrent neural network with user-profile attention and temporal attention mechanisms. Moreover, exercise recommendation systems are significantly different from streaming recommendation systems in that we are not able to collect click feedback from the participants in exercise recommendation systems. Thus, we propose a real-time, expert-in-the-loop active learning procedure. The active learner calculates the uncertainty of the recommendation system at each time step for each user and asks an expert for recommendation when the certainty is low. In this paper, we derive the probability distribution function of marginal distance, and use it to determine when to ask experts for feedback. Our experimental results on a mHealth and MovieLens datasets show improved accuracy after incorporating the real-time active learner with the recommendation system.

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T-RECSYS: A Novel Music Recommendation System Using Deep Learning

Cited by 63 publications

References 76 publications

Deep Learning Application Pros And Cons Over Algorithm

Deep Learning Application Pros And Cons Over Algorithm

A multi-preference integrated algorithm (MPIA) for the deep learning-based recommender framework (DLRF)

Real-Time Learning from an Expert in Deep Recommendation Systems with Application to mHealth for Physical Exercises

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