Ordinal consistency based matrix factorization model for exploiting side information in collaborative filtering

In designing modern recommender systems, item feature information (or side information) is often ignored as most models focus on exploiting rating information. However, the side information is equally essential for capturing users' interests in items. Also, the recommender systems that use side information partially process the feature information by ignoring the locality-preserving property of item features. This study proposes an approach for collaborative filtering by applying an ordinal consistency-based matrix factorization (MF) model to maintain the locality-preserving property of item features to counter this problem. The ordinal consistency condition is implied using a loss function to the item features. Using MF removes the redundancy and inconsistency in item features, producing good results in calculating similarity information for recommendations. We have used five benchmark datasets to evaluate and compare the proposed model. Results obtained using the experiments suggest significant improvement in performance compared to related baselines.