Comparing performance of collaborative filtering algorithms

Abstract

Recommender systems are widely used for making personalized recommendations for products or services during a live interaction nowadays. Collaborative filtering is the most successful and commonly used personalized recommendation technology. The open nature of collaborative recommender systems provides an opportunity for malicious users to access the systems with multiple fictitious identities and insert a number of fake user profiles in an attempt to bias the recommender systems in their favor.In the proposed work, we will explore to combine the user trust mechanism with collaborative filtering algorithm for the purpose of improving the robustness of recommendation algorithm and ensuring the quality of recommendations. We propose computational model of trust and then a collaborative filtering algorithm based on it. This User Trust Based collaborative Filtering Algorithm is further modified considering impact of time on the user ratings. The performance of all the three algorithms is compared in terms of Mean Absolute Error between the actual and predicted rating by the respective recommender system.

An Improved Collaborative Filtering Algorithm Combining User Activity Level

“…Although widely used, user-based CF has defects [5]. Firstly, new added items or new users with no rating record can never be recommended.…”

Weighted interest degree collaborative recommendation algorithm based on association rules

“…The development of the recommended system has so far had a history of more than 20 years, due to its large application requirements, the recommendation system has received extensive attention [19]. As a filtering mechanism [15], the recommendation system is an important means to solve information overload [18]. The core of the recommendation system is the recommendation algorithm [17].…”