A Survey Paper on Recommender Systems

TL;DR

This survey reviews various recommender system techniques, evaluates their efficiency and accuracy, and presents experimental insights, highlighting the need for scalable and high-quality recommendations in large data environments.

Contribution

It provides a comprehensive overview of collaborative filtering methods, analyzes their performance, and introduces a new approach to improve recommendation quality.

Findings

Item-based collaborative filtering effectively captures item relationships.

User-based approaches identify user similarity for personalized recommendations.

Experimental application of data mining algorithms enhances recommender system performance.

Abstract

Recommender systems apply data mining techniques and prediction algorithms to predict users' interest on information, products and services among the tremendous amount of available items. The vast growth of information on the Internet as well as number of visitors to websites add some key challenges to recommender systems. These are: producing accurate recommendation, handling many recommendations efficiently and coping with the vast growth of number of participants in the system. Therefore, new recommender system technologies are needed that can quickly produce high quality recommendations even for huge data sets. To address these issues we have explored several collaborative filtering techniques such as the item based approach, which identify relationship between items and indirectly compute recommendations for users based on these relationships. The user based approach was also studied, it identifies relationships between users of similar tastes and computes recommendations based on these relationships. In this paper, we introduce the topic of recommender system. It provides ways to evaluate efficiency, scalability and accuracy of recommender system. The paper also analyzes different algorithms of user based and item based techniques for recommendation generation. Moreover, a simple experiment was conducted using a data mining application -Weka- to apply data mining algorithms to recommender system. We conclude by proposing our approach that might enhance the quality of recommender systems.