Personalized learning full-path recommendation model based on LSTM neural networks

Zhou, Yuwei; Huang, Changqin; Hu, Qintai; Zhu, Jia; Tang, Yong

doi:10.1016/j.ins.2018.02.053

Cited by 190 publications

(80 citation statements)

References 17 publications

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“…LSTM is considered one of the most successful variant of RNN, with the capability of capturing long-term relationships in a sequence and suffering from the vanishing gradient problem. So far, LSTM models have achieved tremendous success in sequence modelling tasks [20,21].…”

Section: Related Workmentioning

confidence: 99%

Joint Relational Dependency Learning for Sequential Recommendation

Wang

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Sequential recommendation leverages the temporal information of users' transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term transition dependencies contain the information of partial item orders, while long-term transition dependencies infer long-range user preference, the two dependencies are mutually restrictive and complementary. Although some work investigates unifying both long-term and short-term dependencies for better performance, they still neglect the fact that short-term interactions are multi-folds, which are either individual-level interactions or unionlevel interactions. Existing sequential recommendations mainly focus on user's individual (i.e., individual-level) interactions but ignore the important collective influence at union-level. Since union-level interactions can reflect that human decisions are made based on multiple items he/she has already interacted, ignoring such interactions can result in the disability of capturing the collective influence between items. To alleviate this issue, we proposed a Joint Relational Dependency learning (JRD-L) for sequential recommendation that exploits both long-term and short-term preferences at individual-level and union-level. Specifically, JRD-L combines long-term user preferences with short-term interests by measuring shortterm pair relations at individual-level and union-level. Moreover, JRD-L can alleviate the sparsity problem of union-level interactions by adding more descriptive details to each item, which is carried by individual-level relations. Extensive numerical experiments demonstrate JRD-L outperforms state-of-the-art baselines for the sequential recommendation.

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Section: Related Workmentioning

confidence: 99%

Joint Relational Dependency Learning for Sequential Recommendation

Wang

et al. 2020

Advances in Knowledge Discovery and Data Mining

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“…GRU4Rec [11]). For example, Zhou et al [40] introduced Recurrent Neural Network (RNN) to predict the expectation of the whole path for learner groups. Some researches proposed to enhance the recommendation strategy by explicitly using cognitive structure.…”

Section: Related Workmentioning

confidence: 99%

“…genetic epistemology [26]), meanwhile the knowledge structure captures the cognitive relations among the learning items. However, existing methods for adaptive learning only utilize either knowledge level [33,40] or knowledge structure [38,41]. Although these methods have made a great success in adaptive learning, there are some limitations of them.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Cognitive Structure for Adaptive Learning

Liu

Tong

Liu

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

106

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Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.

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“…The Long-Short Term Memory model (LSTM) is a kind of Recurrent Neural Network (RNN) [10,12]. It proposes an improvement to the gradient disappearance problem in the RNN model and replaces the hidden layer nodes in the original RNN model with one memory unit.…”

Section: Long-short Term Memorymentioning

confidence: 99%

Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

Zhao

Zhu

Wan

et al. 2018

Water

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Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values.

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Personalized learning full-path recommendation model based on LSTM neural networks

Cited by 190 publications

References 17 publications

Joint Relational Dependency Learning for Sequential Recommendation

Joint Relational Dependency Learning for Sequential Recommendation

Exploiting Cognitive Structure for Adaptive Learning

Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

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