Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation

Lin, Yuanguo; Lin, Fan; Zeng, Wenhua; Xiahou, Jianbing; Li, Li; Wu, Pengcheng; Liu, Yong; Chen, Miao

doi:10.1016/j.knosys.2022.108546

Cited by 22 publications

(8 citation statements)

References 59 publications

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“…They also proposed a new extended GRU cell named EMGRU, which can efficiently enhance the recommendation accuracy by incorporating additional historical information to address the warm-start scenario in long-term prediction. Another similar work on policy gradient method with dynamic recurrent [70] is built in which a profile constructor with autonomous learning ability is designed to make personalized course recommendation. The approach is proposed to address the exploration-exploitation trade-off issue in constructing user profiles while the recurrent scheme by context-aware learning exploit the user's current knowledge and explore the future preferences.…”

Section: A Multi-objective Recommendation Approachesmentioning

confidence: 99%

Multiobjective Deep Reinforcement Learning for Recommendation Systems

et al. 2022

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Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start problem from collaborative filtering, it also vulnerable to highly sparse environment, while the evolutionary algorithm suffers from premature convergence and curse of dimensionality. These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. In this study, the performances of proposed DRL approaches that based on Deep Q-Network in MO recommendation problem were investigated. The approaches were evaluated with movie recommendation dataset by using three conflicting metrics, namely precision, novelty, and diversity. The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multiobjective approach based on evolutionary algorithm (PMOEA). Although PMOEA algorithm secured higher average value in precision, it has lower values of novelty and diversity than the proposed DRL approaches. The DRL approaches surpassed the benchmark results in average of maximum novelty and the average of mean diversity metrics, the optimization between accuracy and non-accuracy metrics is inevitable. In addition, the experiments revealed that incorporation of user latent features enhanced the recommendation quality.

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Section: A Multi-objective Recommendation Approachesmentioning

confidence: 99%

Multiobjective Deep Reinforcement Learning for Recommendation Systems

et al. 2022

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“…Recently, Deep Reinforcement Learning (DRL) techniques are widely applied to various scientific problems and, in several tasks, perform superior to humans. Lin et al [6] presented a hierarchical reinforcement learning with dynamic recurrent mechanism for course recommender systems. The author in [7] also designed a DRL method based on Actor-Critic (AC) framework for knowledge recommendation.…”

Section: Recommender Systemmentioning

confidence: 99%

“…Online course recommendation has attracted widespread attention in the area of intelligent education. Along this line, Lin et al [6] presented a hierarchical reinforcement learning with dynamic recurrent mechanism for course recommender systems, which designs a profile constructor to efficiently trace the learner's preferences for personalized course recommendation. By treating learning path recommendation task as a Markov Decision Process, Liu et al [7] developed a cognitive structure enhanced framework using actor-critic algorithm that can generate a suitable learning path to different learners.…”

Section: Reinforcement Learning In Educationmentioning

confidence: 99%

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Fully adaptive recommendation paradigm: top-enhanced recommender distillation for intelligent education systems

Ren

Liang

Shang

et al. 2022

Complex Intell. Syst.

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Top-N recommendation has received great attention in assisting students in providing personalized learning guidance on the required subject/domain. Generally, existing approaches mainly aim to maximize the overall accuracy of the recommendation list while ignoring the accuracy of highly ranked recommended exercises, which seriously affects the students’ learning enthusiasm. Motivated by the Knowledge Distillation (KD) technique, we skillfully design a fully adaptive recommendation paradigm named Top-enhanced Recommender Distillation framework (TERD) to improve the recommendation effect of the top positions. Specifically, the proposed TERD transfers the knowledge of an arbitrary recommender (teacher network), and injects it into a well-designed student network. The prior knowledge provided by the teacher network, including student-exercise embeddings, and candidate exercise subsets, are further utilized to define the state and action space of the student network (i.e., DDQN). In addition, the student network introduces a well-designed state representation scheme and an effective individual ability tracing model to enhance the recommendation accuracy of top positions. The developed TERD follows a flexible model-agnostic paradigm that not only simplifies the action space of the student network, but also promotes the recommendation accuracy of the top position, thus enhancing the students’ motivation and engagement in e-learning environment. We implement our proposed approach on three well-established datasets and evaluate its Top-enhanced performance. The experimental evaluation on three publicly available datasets shows that our proposed TERD scheme effectively resolves the Top-enhanced recommendation issue.

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“…In each iteration, the offset vectors generated by the previous iteration, which were added to the current sampling location to update the location. To avoid the damage caused by traditional hard ViT segmentation in the image structure 30–33 and to focus attention like the human eye on the area of interest, reducing irrelevant information interference due to organ deformation, unclear boundaries of polyps and colon folds, fragmentation, feces, and other factors. The PS module during training avoids the poor performance of reinforcement learning methods 30 on complex datasets.…”

Section: Introductionmentioning

confidence: 99%

TACT: Text attention based CNN‐Transformer network for polyp segmentation

Zhao,

Li,

Hua

2023

Int J Imaging Syst Tech

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Colorectal cancer (CRC) has been one of the top three disease in the world in terms of incidence for many years. Therefore, how to prevent and treat CRC has become a topic of concern for an increasing number of people, and colonoscopy is the most effective detection method in polyp examination. According to studies, 90% of CRC is caused by adenomatous polyps of the large intestine. In clinical practice, the diversity of polyps' size, number, and shape and the unclear boundary between polyps and colon folds can reduce the operator's accuracy of polyps segmentation and lead to a higher rate of missed diagnosis. To better address the inaccurate segmentation or high miss rate due to the above factors, we propose a text attention‐based CNN‐Transformer network for polyp segmentation (TACT) network to process the images in a way that minimizes operator subjectivity and miss rate. The network is based on the CNN‐Transformer structure, and on this basis, a fully attention progressive sampling module is added to more accurately divide the polyp boundary. Moreover, an auxiliary text classification task was added to focus on polyp size and number features in the form of text attention, which more effectively copes with the segmentation tasks of different sizes and different numbers of polyps. After comparing with multiple state‐of‐the‐art segmentation methods in four challenging datasets, our proposed TACT improves segmentation accuracy for polyps of different sizes in different datasets.

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Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation

Cited by 22 publications

References 59 publications

Multiobjective Deep Reinforcement Learning for Recommendation Systems

Multiobjective Deep Reinforcement Learning for Recommendation Systems

Fully adaptive recommendation paradigm: top-enhanced recommender distillation for intelligent education systems

TACT: Text attention based CNN‐Transformer network for polyp segmentation

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