Online Resource Recommendation System

EasyChair Preprint no. 12800

Abstract

In recent years, the use of online resources for learning has increased significantly due to their convenience and accessibility. However, with the vast amount of resources available online, it can be challenging for users to identify and access the most relevant and valuable resources. This has led to the development of recommendation systems that can generate personalized recommendations based on user preferences and behaviour. This project aims to develop an online resource recommendation system using content-based filtering, which is a widely used method for generating personalized recommendations based on user behaviour and preferences. content-based filtering, works by identifying users who have similar interests and behaviour and recommending resources that these similar users have found valuable. The proposed system will also use item-based content-based filtering to recommend resources that are similar to those that the user has already interacted with. The system will be developed using Python programming language and will use the open-source library for content-based filtering algorithm implementation. The system will also incorporate features such as resource ratings to enhance the quality and relevance of the recommendations generated. The success of the proposed system will be measured by the extent to which it can enhance the quality of online learning experiences by providing users with personalized and relevant recommendations. The proposed system has the potential to promote engagement and motivation among users and can serve as a valuable tool for lifelong learning. Keywords: online learning, recommendation systems, content-based filtering, personalized recommendations, user preferences, resource ratings, online learning experiences.

Keyphrases: content-based filtering, online learning, online learning experiences, Personalized Recommendations, Recommendation Systems, resource ratings, user preferences

@Booklet{EasyChair:12800,
  author = {Gunasekhar Vemula and Mehar Swaraj Valpishetty and Brahmananda Reddy Yama and Sai Srujan Macharla and Hemlata Patel and Komal Bonde},
  title = {Online Resource Recommendation System},
  howpublished = {EasyChair Preprint no. 12800},

  year = {EasyChair, 2024}}