Abstract
The system available today which generates recommendations is supported by the user’s information collected within the past. It does not include intention at a specific time. The utilization of data mining and machine learning algorithms offers real-time recommendations employing a fitness function and models that estimate the suitability of recommended lists. The utilization of a social network provides user-generated content during a much more convenient scenario. It also uses a user experience. Recommendation systems play an outsized role in providing quality to those. In this paper, we present how recommendations are often produced using data processing and machine learning algorithms. Three systems with varied applications are addressed during this paper. First, a recommendation is used for better crop cultivation using certain parameters of concern with the assistance of association rule mining and genetic algorithms. Second, a way to recommend videos for advertisements from those available with titles, descriptions, and hashtags as extracted features is presented. These use machine learning-associated multi-label classification algorithm. Finally, an anti-vice recommendation system that uses neural networks to get recommendations is brought forward. Altogether the three cases, it is observed that the accuracy and efficiency of recommendations are upgraded.
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I would wish to say a “Big Thank You” to my Institution for all the assistance extended to me during my work and documentation of it.
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Lobo, L.M.R.J., Birbal, K.M. (2022). Generating Recommendations for Various Problems Using Data Mining and Machine Learning Algorithms. In: Bindhu, V., Tavares, J.M.R.S., Du, KL. (eds) Proceedings of Third International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-16-8862-1_63
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