Event-based incremental recommendation via factors mixed Hawkes process

Cui, Zhihong; Sun, Xiangguo; Pan, Li; Liu, Shijun; Xu, Guandong

doi:10.1016/j.ins.2023.119007

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…Equation ( 5) indicates that for a student u, if the response to a question i is 1, then i is considered to assess "knowing" more than other questions. Under the conditions set by the partial order > + u , the R matrix can be transformed into a set of comparative questions D u using Equation (6).…”

Section: "Knowing" Modelmentioning

confidence: 99%

“…With the continuous advancement of internet and artificial intelligence technologies, AI algorithms have significantly impacted various aspects of our lives, such as smart city development [1][2][3], energy management [4,5], and financial services [6,7]. Similarly, the field of education has been continually evolving due to the influence of artificial intelligence [8][9][10][11][12][13] and internet technologies [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Parallel Prediction Method of Knowledge Proficiency Based on Bloom’s Cognitive Theory

Zhang,

Mao,

Liu

et al. 2023

Mathematics

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Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom’s cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including “knowing”, “understanding”, and “application”, thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom’s Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of “knowing”, “understanding”, and “applying” to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students’ knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of “understanding” and “application”, the notions of “knowledge group” and “higher-order knowledge group” are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students’ knowledge proficiency, affirming the scientific and meaningful integration of Bloom’s cognitive hierarchy in knowledge proficiency assessment.

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Section: "Knowing" Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel Prediction Method of Knowledge Proficiency Based on Bloom’s Cognitive Theory

Zhang,

Mao,

Liu

et al. 2023

Mathematics

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“…The exploration of hypergraph representation for sociological analysis emphasizes the richness of social interactions and environments, providing a novel approach to understanding complex sociological phenomena through data mining techniques [18]. The factor-mixed Hawkes process (FMHP) for event-based incremental recommendations introduces a nuanced understanding of event generation, considering intrinsic, external, and historical intensities, thereby enhancing recommendation systems [19]. Furthermore, graph-masked autoencoders (GMAEs) represent a significant step forward in learning graph representations, adopting a self-supervised, transformer-based model that addresses the challenges of training deep transformers from scratch [20].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks

Cai,

Ma,

Hou

et al. 2024

Electronics

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In the rapidly evolving domain of question answering systems, the ability to integrate machine comprehension with relational reasoning stands paramount. This paper introduces a novel architecture, the Dependent Syntactic Semantic Augmented Graph Network (DSSAGN), designed to address the intricate challenges of multi-hop question answering. By ingeniously leveraging the synergy between syntactic structures and semantic relationships within knowledge graphs, DSSAGN offers a breakthrough in interpretability, scalability, and accuracy. Unlike previous models that either fall short in handling complex relational paths or lack transparency in reasoning, our framework excels by embedding a sophisticated mechanism that meticulously models multi-hop relations and dynamically prioritizes the syntactic–semantic context.

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HGTHP: a novel hyperbolic geometric transformer hawkes process for event prediction

Xie,

2023

Appl Intell

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Event-based incremental recommendation via factors mixed Hawkes process

Cited by 9 publications

References 46 publications

Parallel Prediction Method of Knowledge Proficiency Based on Bloom’s Cognitive Theory

Parallel Prediction Method of Knowledge Proficiency Based on Bloom’s Cognitive Theory

Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks

HGTHP: a novel hyperbolic geometric transformer hawkes process for event prediction

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