An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniques

Recommender systems provide useful recommendations to a collection of users for items or products that might be of concern or interest to them. Several techniques have been proposed for recommendation such as collaborative filtering, content-based, knowledge-based, and demographic filtering. Each of these techniques suffers from scalability, data sparsity, and cold-start problems when applied individually resulting in poor recommendations. This paper proposes an adaptive hybrid recommender system that combines multiple techniques together to achieve some synergy between them. Collaborative filtering and demographic techniques are combined in a weighted linear formula. Different experiments applied using movieLen dataset confirm that the proposed adaptable hybrid framework outperforms the weaknesses resulted when using traditional recommendation techniques.