ML Based Social Media Analysis and Recommendation

Author(s)	Mr. Laukik Pawar, Mr. Sehej Chitale, Mr. Prajwal Gadge, Mr. Shreyas Fegade, Prof. Pramila Mate
Country	India
Abstract	This project focuses on developing anintelligent content recommendation system thatanalyzes user behavior across social mediaplatforms to deliver personalized, timely contentsuggestions. Utilizing machine learning, naturallanguage processing, and data analysis, the systemovercomes cold start challenges to ensure accuraterecommendations for new users and content.The framework has three core components: datacollection, analysis, and recommendation. Userliked and saved content from platforms likeYouTube and Reddit are grouped using K-Meansclustering, revealing key user interest themes.Temporal analytics track user interactions overtime, dynamically adjusting recommendations toalign with peak engagement periods.
Keywords	Content Recommendation ; Machine Learning; User Behavior Analysis; Clustering Algorithms; Social Media Data; Personalized Recommendations; Time-Based Recommendations, Data Analysis, Content Consumption Patterns
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-20
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.42305
Short DOI	https://doi.org/g9f7pk

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International Journal For Multidisciplinary Research