Collaborative Filtering Recommendation Based on Statistical Implicative Analysis

Huynh, Hiep Xuan; Phan, Nghia Quoc; Duong-Trung, Nghia; Nguyen, Ha Thu

doi:10.1007/978-3-030-63119-2_19

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…• Mean Absolute Error (MAE) [14][23]. The mean absolute error between real rating value 𝑟 𝑖𝑗 and the predicted rating value 𝑟̂𝑖 𝑗 is calculated by fomular (12).…”

Section: B Evaluationmentioning

confidence: 99%

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Energy-based Collaborative Filtering Recommendation

Tran¹,

Phan²,

Huynh³

2022

IJACSA

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The core value of the recommendation model is the using of the measures to measure the difference between the jumps (e.g. pearson), some other studies based on the magnitude of the angle in space (e.g. cosine), or some other studies study the level of confusion (e.g. entropy) between users and users, between items and items. Recommendation model provides an important feature of suggesting the suitable items to user in common operations. However, the classical recommendation models are only concerned with linear problems, currently there is no research about nonlinear problems on the basis of potential/energy approach to apply for the recommendation model. In this work, we mainly focus on applying the energy distance measure according to the potential difference with the recommendation model to create a separate path for the recommendation problem. The theoretical properties of the energy distance and the incompatibility matrix are presented in this article. Two experiment scenarios are conducted on Jester5k, and Movielens datasets. The experiment result shows the feasibility of the energy distance measures/ the potential in the recommendation systems.

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“…• Mean Absolute Error (MAE) [14][23]. The mean absolute error between real rating value 𝑟 𝑖𝑗 and the predicted rating value 𝑟̂𝑖 𝑗 is calculated by fomular (12).…”

Section: B Evaluationmentioning

confidence: 99%

“…pearson) [19] [2], or the recommendation systems based on the confuse of one user with another (e.g. entropy) [4], some other recommendation systems based on the statistical implication [12] [18].…”

Section: Introductionmentioning

confidence: 99%

Energy-based Collaborative Filtering Recommendation

Tran¹,

Phan²,

Huynh³

2022

IJACSA

Self Cite

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“…It uses statistical independence (among the variables) as a baseline, to measure the similarity between variables (for the clustering task) and the quality of rules (for association rule task). SIA was initially developed in the field of Didactics of Mathematics [12], and is now being used in a wider range of data and domains [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote

2021

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In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e.g., CART, C4.5, J48) to more complex, less interpretable and more accurate ones (e.g., neural networks, SVM). In this tradeoff between interpretability and accuracy, we propose a new classifier based on association rules, that is to say, both easy to interpret and leading to relevant accuracy. To illustrate this proposal, its performance is compared to other widely used methods on six open access datasets.

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