What rating they will probably give: A cognitive diagnosis approach for recommending items based on polytomous responses and latent attributes

Recommendation Systems have become prevalent in recent years, attracting the attention of researchers to investigate different methods to filter relevant information for users. This information is not always explicit and different proposals have emerged to obtain the latent values of individuals through their behavior. In educational areas, latent attributes of test-takers can be acquired by psychometric models such as the Cognitive Diagnostic Model. These models attempt to create a user’s profile in order to explore the connections between students and subjects, just like a recommendation system does with its users and the products to be recommended. The objective of this work is to develop a new recommendation approach that incorporates Cognitive Diagnostic Models applied to data from media defined by discrete content (such as genres in movies and series) in order to generate its polytomous response in the form of the rating prediction that a user would give to each item. The proposed approach was applied to two datasets (MovieLens20M Dataset and Anime Recommendation Database). The new proposal was also considered with additional information regarding the popularity of the items, in an enhanced version of our model, and compared to classic recommendation systems found in the literature. Finally, this work also explored the performance of the models in ranking items to be recommended for the users. In general, the new method obtained better results than the classic recommendation ones for both the predicted rating and the item ranking. •A new approach in Recommendation Systems applying Cognitive Diagnosis Model•Proposal of an enhanced model applying Cognitive Diagnosis Model and Item Popularity•Exploration of a bridge between Recommendation Systems and Psychometric models