A Brief Analysis of Collaborative and Content Based Filtering Algorithms used in Recommender Systems

Abstract

In the modern age and many prestigious applications use the recommendation method to play an important role. The system of recommendations collected apps, built a global village and provided enough information for development. This paper presents an overview of the approaches and techniques produced in the recommendation framework for collaborative filtering. Collaborative filtering, material and hybrid methods were the method of recommendation. In producing personalised recommendation the technique of collaborative filtering is particularly effective. There have been several algorithms over ten years of study, but no distinctions have been made between the various strategies. Indeed, there is not yet a widely agreed way to test a collaborative filtering algorithm. In this work we compare various literature techniques and review each one’s characteristics to emphasise their key strengths and weaknesses.

Crop and Fertilizer Recommendation System

“…Technological Changes in the agricultural sector tend to be very slow. It is often observed By reviewing the considerations and methodologies used in the research papers [10][11][12][13][14][15][16][17][18], they have tried to develop ML models to recommend crops and fertilizers to produce a good yield. Some considered building a web application to ease the process of accessing the recommendation system.…”

Paddy Crop Disease Detection Using Deep Learning

“…There have been very limited advancements in the use of machine learning for detecting plant leaf diseases, and classification. [18][19][20][21][22][23][24][25][26]…”

A Systematic Approach to Detect Parkinson’s Disease using Traditional and Ensemble Machine Learning Techniques

“…The XGBClassifier achieved an accuracy of 94.87% proving that it is better than traditional algorithms. [6][7][8][9][10][11][12][13][14] ISSN PRINT 2319 1775 Online 2320 7876…”