Item feature refinement using matrix factorization and boosted learning based user profile generation for content-based recommender systems

A content-based recommender system uses essential item features that play a crucial role in building quality user preference profiles. However, in most real-world datasets, the item features are highly inconsistent and sparse, making it challenging to develop efficient user profiles. Additionally, the user preference profiles created by individual learners fail to learn from the misclassification of user ratings and preferences. Thus, to resolve these problems, this paper suggests a two-fold approach to improve the performance of the content-based recommender systems. The first approach is the refinement of the existing sparsity and inconsistencies in item features using matrix factorization. The second approach is the generation of individual preference profiles using iterative boosting of multiple weak learners for penalizing the misclassification of ratings. The suggested method is tested via benchmark recommender system datasets such as ML-1M, Last.fm, and Netflix. The results obtained during experiments show a significant improvement in recommendation quality over the state-of-the-art content-based recommender system models. • The model applies item feature refinement before applying content-based filtering. • The use of matrix factorization avoids the sparsity in item feature information. • The refined item features are used for generating similarity information. • User profiles are built using the ensemble learning technique for recommendation.