Integrating KNN and Gradient Boosting Decision Tree for Recommendation

Du, Qiming; Li, Nan; Yang, Shudan; Sun, Daozhu; Liu, Wenfu

doi:10.1109/iaeac50856.2021.9390647

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The BART has prior with tree structure and leaf parameters, given that tree structure and error variance are independent of tree structure and leaf parameters [30]. The prior is combined with likelihood to make posterior, and the posterior is able to build a model with better performance than the other models such as gradient boosting tree [35,36]. Therefore, we used the BART that relies on the Bayesian probability model to obtain better performance than others.…”

Section: Discussionmentioning

confidence: 99%

Patent Analysis Using Bayesian Data Analysis and Network Modeling

Park

Jun

2022

Applied Sciences

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Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology.

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Section: Discussionmentioning

confidence: 99%

Patent Analysis Using Bayesian Data Analysis and Network Modeling

Park

Jun

2022

Applied Sciences

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“…Semantic means that the meaning of data can be discovered by computers. Currently, many machine learning methods have been used for the model-based CF, such as the Back ward Propagation (BP) neural network, Adaptive learning, and linear classifier [7]. Currently, CF based recommendation techniques have been applied in a variety of areas, such as music recommendation, news recommendation, product recommendation, etc.…”

Section: Introductionmentioning

confidence: 99%

User Feature Similarity Supported Collaborative Filtering for Page Recommendation Using Hybrid Shuffled Frog Leaping Algorithm

2023

IJIES

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The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to. To overcome the product overload of online shoppers, a variety of recommendation methods have been developed. Collaborative Filtering (CF) is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations. This work proposes a recommendation methodology based on Web Usage Mining (WUM), and machine learning methods to enhance the recommendation quality and the system performance of current CF-based recommender systems. WUM populates the rating database by tracking learners' behaviors on the Web, thereby leading to better quality recommendations. The data is collected from user profile and their preferences and also the weblink and usage. Based on the CF and WUM data, the recommendations are provided. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers is used to improve the performance of searching for nearest neighbors through Shuffled Frog Leaping Algorithm (SFLA). Experimental results show that the proposed model is effective and can enhance the performance of recommendation. Results show that the proposed SFLA-XGBoost has higher average sensitivity: rating <=15 for CF by 11.49% for RF, by 7.52% for XGBoost and by 4.58% for proposed SFLA-RF respectively.

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