AdaBoosting for case-based recommendation system

Singal, Swati Mittal; Tejal,; Juneja, Bhawna

doi:10.1109/incite.2016.7857591

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Several studies utilized a range of ML techniques and related technologies to classify and cluster user preferences. The AdaBoost machine learning algorithm was used by [23]. Additionally, clustering, decision trees, and association rule mining were used by [24].…”

Section: Data Science and Machine Learning Techniques Used In Develop...mentioning

confidence: 99%

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Necula

Păvăloaia

2023

Applied Sciences

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Electronic commerce has a strong connection with recommendation processes. There are various forms of recommendations, ranging from virtual assistants to online suggestions made in real time. Different algorithms and technologies are utilized for each form, and the choice of technique is dependent on the task at hand. For instance, artificial intelligence may utilize deep learning or machine learning techniques. The type of data also plays a role in determining the techniques used. Predictive modeling is applied to textual data, while image data requires image processing followed by AI algorithms for prediction. This study aimed to investigate the extent to which artificial intelligence is utilized in recommender systems for electronic commerce, as well as the current and future trends in the field. This was achieved through a systematic literature review of scientific articles from the past decade, using WosViewer for data collection and the Bibliometrix R package for analysis. The findings demonstrate that artificial intelligence works in conjunction with other technologies, such as blockchain, virtual reality, and augmented reality, to enhance the consumer experience throughout the e-commerce process.

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Section: Data Science and Machine Learning Techniques Used In Develop...mentioning

confidence: 99%

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Necula

Păvăloaia

2023

Applied Sciences

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“…Several Machine Learning research were employed to classify and cluster the user preferences. Singal et al [12], employed AdaBoost machine learning method. Additionally, Exenberger et al [13], employed clustering, decision trees, and association rule mining.…”

Section: Hybrid-basedmentioning

confidence: 99%

Customer Relationship Management for Better Insights with Descriptive Analytics

Zulkiflee,

Haw,

et al. 2023

AAIML

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The Customer Relationship Management (CRM) dashboard with analytics capabilities may be useful for businesses desire to understand consumer behaviour and gain competitive advantages. This user-friendly dashboard allows organizations to efficiently collect and evaluate important data, including customer interactions, purchase histories, and demographics. By leveraging various visualization techniques, such as histogram, bar charts, line charts, and heatmaps, the CRM dashboard displays statistics, enabling businesses to understand more about customer journeys, patterns, and behavioural trends. The integrated recommender system on the CRM dashboard is also crucial. Based on their historical interactions and buying behaviours, this system recommends tailored products or services to customers, enhancing engagement and ultimately increasing sales. The methodology used in this research is structured based on a standard data analytics procedure. The methods used to gather and evaluate the data are described in the data gathering and analysis. It contains details on the data gathering, the steps used to clean and prepare the data, and the statistical or analytical 1482

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“…According to Singal et al (2016), recommender systems are ways for web personalisation and crafting the browsing experience to the users' specific needs and are tools for communicating with large and complicated information spaces. It gives a personalised view of these spaces, ranking items likely to be of interest to the user.…”

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confidence: 99%

On Application of Case-Based Reasoning to Personalise Learning

Mamčenko¹,

Kurilovas²,

Krikun³

2019

Informatics in Education

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The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for them. First of all, systematic literature review on application of CBR and its possible implementation to personalise learning was performed in the paper. After that, methodology on CBR application to personalise learning is presented where learning styles play a dominate role as key factor in proposed personalised intelligent learning system model based on students profiling and personalised learning process model. The algorithm (the sequence of steps) to implement this model is also presented in the paper.

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AdaBoosting for case-based recommendation system

Cited by 4 publications

References 7 publications

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Customer Relationship Management for Better Insights with Descriptive Analytics

On Application of Case-Based Reasoning to Personalise Learning

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