Design of Personalized Content Recommendation System for English Reading Based on Deep Learning and Intelligent Algorithms

The limited availability of data resources has significantly hindered the education sectors, widely rely and depend significantly on them in driving the element of product as well as corporate growth in innovation in this age. Personalized content recommendation systems for English reading are versatile information filtering tools that analyze prior user interactions with various items to generate user interest models. These systems assist users in discovering and promoting material that aligns with their interests, especially in a world where there is an overwhelming amount of information available. This study seeks to design a personalized content recommendation system for English reading based on deep learning and intelligent algorithms. The findings indicate that the values of different similarity computations change as the number of closest neighbors gradually increases, with this approach reaching its peak value at approximately 60 neighbors. Therefore, it can be established that the integration of this algorithms into the design of the personalized recommendation system would significantly enhance the precision of the suggestions.