International Journal of Algorithms Design and Analysis Review Review Article
Enhancing User Engagement and Content Relevance: A Novel Approach to Social Media Post Recommendation System
Abstract
The social media post recommendation system is an innovative solution aimed at optimizing content delivery for users in today's digital age. Its primary motive is to tailor online experiences, ensuring users receive posts most relevant to their preferences. Various machine learning algorithms are employed to suggest related posts. This system leverages advanced algorithms and analytics, producing key results that highlight user engagement metrics and content relevance. Preliminary findings of this study suggest that a bespoke content delivery method significantly enhances user engagement. This system has the potential to redefine social media content recommendations. The primary aim of the system is to enhance user experience by tailoring online experiences to individual preferences. This entails providing users with highly relevant content to boost engagement and satisfaction. The system employs multiple machine learning algorithms to suggest related posts. These algorithms analyze user behavior, preferences, and interactions to generate personalized recommendations. By leveraging advanced algorithms and analytics, the system aims to produce key insights into user engagement metrics and content relevance. Overall, the social media post recommendation system represents a comprehensive and forward-thinking approach to optimizing content delivery in the digital age. By leveraging advanced technology and analytics, the system aims to provide users with a more personalized and engaging online experience.
Keywords
References (10)
- Geng X, Zhang H, Bian J, Chua TS. Learning Image and User Features for Recommendation in Social Networks. 2015 IEEE International Conference on Computer Vision (ICCV). 2015:4274-4282. doi:10.1109/iccv.2015.486
- Sejal D, Rashmi V, Venugopal KR, Iyengar SS, Patnaik LM. Image recommendation based on keyword relevance using absorbing Markov chain and image features. International Journal of Multimedia Information Retrieval. 2016;5(3):185-199. doi:10.1007/s13735-016-0104-9
- Anandhan A, Shuib L, Ismail MA, Mujtaba G. Social Media Recommender Systems: Review and Open Research Issues. IEEE Access. 2018;6:15608-15628. doi:10.1109/access.2018.2810062
- Yin P, Zhang L. Image Recommendation Algorithm Based on Deep Learning. IEEE Access. 2020;8:132799-132807. doi:10.1109/access.2020.3007353
- Fayyaz Z, Ebrahimian M, Nawara D, Ibrahim A, Kashef R. Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Applied Sciences. 2020;10(21):7748. doi:10.3390/app10217748
- Hu Y, Hong Y. SHEDR: An End-to-End Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media. SSRN Electronic Journal. 2020. doi:10.2139/ssrn.3677461
- Wan M, Ni J, Misra R, McAuley J. Addressing Marketing Bias in Product Recommendations. Proceedings of the 13th International Conference on Web Search and Data Mining. 2020:618-626. doi:10.1145/3336191.3371855
- Zhang Y, Yamasaki T. Style-Aware Image Recommendation for Social Media Marketing. Proceedings of the 29th ACM International Conference on Multimedia. 2021:3106-3114. doi:10.1145/3474085.3475453
- Du S, Chen Z, Wu H, Tang Y, Li Y. Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks. Complexity. 2021;2021(1). doi:10.1155/2021/5196190
- Chakrabarti P, Malvi E, Bansal S, Kumar N. Hashtag recommendation for enhancing the popularity of social media posts. Social Network Analysis and Mining. 2023;13(1). doi:10.1007/s13278-023-01024-9