A USER INTEREST RECOMMENDATION BASED ON COLLABORATIVE FILTERING

This article presents a user interest recommendation system based on collaborative filtering techniques. Collaborative filtering has emerged as one of the most successful techniques for recommendation systems, using user preference and behavioral data to recommend items of interest to target users. Two major collaborative filtering techniques are reviewed in this article: user-based and item-based filtering. The article provides some necessary mathematical formulations about the calculation of similarities and prediction models. First, the effectiveness of collaborative filtering is demonstrated; then, its challenges, such as the cold-start problem and data sparsity, are discussed. Possible improvements in hybrid models and some future research directions are also discussed.