Recommendation system using machine learning

Parvatikar, Suhasini; Parasar, Deepa

doi:10.51483/ijaiml.1.1.2021.24-30

Cited by 2 publications

(1 citation statement)

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“…The paper entitled "Product Recommendation System Using Machine Learning" by Nitin Kamble et al [22] presents a comprehensive exploration into the domain of product recommendation systems powered by machine learning techniques. The importance of tailored recommendations in contemporary e-commerce ecosystems is discussed in the authors' work.…”

Section: IImentioning

confidence: 99%

Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning

Namose,

Hiwarkar

2023

IJRITCC

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This study delves into the realm of leveraging unconventional sources within the domain of Big Data for conducting insightful social and economic analyses. Employing a Data Lifecycle Approach, the research focuses on harnessing the potential of linear regression, random forest, and XGBoost techniques to extract meaningful insights from unconventional data sources. The study encompasses a structured methodology involving data collection, preprocessing, feature engineering, model selection, and iterative refinement. By applying these techniques to diverse datasets, encompassing sources like social media content, sensor data, and satellite imagery, the study aims to provide a comprehensive understanding of social and economic trends. The results obtained through these methods contribute to an enhanced comprehension of the intricate relationships within societal and economic systems, further highlighting the importance of unconventional data sources in driving valuable insights for decision-makers and researchers alike.

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Section: IImentioning

confidence: 99%

Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning

Namose,

Hiwarkar

2023

IJRITCC

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E-Commerce

Sinha

Patro

Hota

2023

Handbook of Research on Applications of AI, Digital Twin, and Internet of Things for Sustainable Development

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The recommendation system works on an idea of suggesting or recommending items, products, books, movies, etc. by analyzing and using some filtering to find the user's interest. To maximize the growth of business and profit gain, users need to be recommended with products belonging to their area of interest. To fulfill this requirement, the recommendation system has been implemented. In this study, the discussion is over recommendation system and how different concepts come to work out individually as well as together for recommendation. In this analysis, the focus is on recommending the method of e-commerce. In that scenario, “cold start problem” comes into consideration. Cold start problems are also studied, and a purposed idea has also been highlighted to reduce cold start problem to some extent. ‘LCW Aspect' is going to execute and analyze user's culture, weather, local scarcity, and focused on solving recommendation problems for new emerging users.

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Recommendation system using machine learning

Cited by 2 publications

References 9 publications

Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning

Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning

E-Commerce

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