Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation

Hong, Daocheng; Li, Yang; Dong, Qian

doi:10.1145/3397271.3401225

Cited by 16 publications

(4 citation statements)

References 5 publications

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“…Work Value-based [18,96,119,135] Policy-based [4,38] Hybrid [125] Policy-based methods. IRecGAN [4] is a model-based method that adopts generative adversarial training to improve the robustness of policy learning.…”

Section: Methodsmentioning

confidence: 99%

“…where the MC U (𝑁 ) represents the sampled 𝑁 sequences from the interaction between U and the agent using the Monte-Carlo tree search algorithm, 𝐷 is the discriminator, 𝑇 is the length of 𝜏, 𝑔 represents the offline data, and data represents the generated data. Hong et al [38] propose NRSS for personalized music recommendation. NRSS uses wireless sensing data to learn users' current preferences.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

Chen¹,

Yao²,

McAuley³

et al. 2021

Preprint

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In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics and open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for readers from academia and industry into the topic and identifies notable opportunities for further research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

Chen¹,

Yao²,

McAuley³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recommender systems based on RL have the advantage of updating the policies during online interaction, which enables the system to generate recommendations that best suit users evolving preferences (Zhao et al, 2019). Examples include news (Zheng et al, 2018), music recommendations (Hong et al, 2020) and personalized learning systems (Shawky & Badawi, 2019).…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

Siyam

Abdallah

2021

Preprint

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Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to solve the problem of selecting the right motivator for children with ASD using Reinforcement Learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov Decision Process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on Applied Behavior Analysis as well as learners’ individual preferences. We use a Q-Learning algorithm to solve the modelled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

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“…Xin et al [30] propose a selfsupervised RL strategy for sequential recommendations. Hong et al [31] introduce a music recommendation framework for adapting to user's current preference based on reinforcement learning in real time during a listening session. Zhao et al [32] present a deep hierarchical reinforcement learning framework to capture the long-term sparse conversion interest at the high level and automatically set abstract goals.…”

Section: Reinforcement Learning In Recommender Systemsmentioning

confidence: 99%

Adaptive Neural Architectures for Recommender Systems

Rafailidis,

Antaris

2020

Preprint

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Deep learning has proved an effective means to capture the non-linear associations of user preferences. However, the main drawback of existing deep learning architectures is that they follow a fixed recommendation strategy, ignoring users' real time-feedback. Recent advances of deep reinforcement strategies showed that recommendation policies can be continuously updated while users interact with the system. In doing so, we can learn the optimal policy that fits to users' preferences over the recommendation sessions. The main drawback of deep reinforcement strategies is that are based on predefined and fixed neural architectures. To shed light on how to handle this issue, in this study we first present deep reinforcement learning strategies for recommendation and discuss the main limitations due to the fixed neural architectures. Then, we detail how recent advances on progressive neural architectures are used for consecutive tasks in other research domains. Finally, we present the key challenges to fill the gap between deep reinforcement learning and adaptive neural architectures. We provide guidelines for searching for the best neural architecture based on each user feedback via reinforcement learning, while considering the prediction performance on real-time recommendations and the model complexity.

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Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation

Cited by 16 publications

References 5 publications

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

Adaptive Neural Architectures for Recommender Systems

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