Recommender systems (RSs) represent one of the manifold applications in which Machine Learning can unfold its potential. Nowadays, most of the major online sites selling products and services provide users with RSs that can assist them in their online experience. In recent years, therefore, we have witnessed an impressive series of proposals for novel recommendation techniques that claim to ensure significative improvements compared to classic techniques. In this work, we analyze some of them from a theoretical and experimental point of view and verify whether they can deliver tangible real improvements in terms of performance. Among others, we have experimented with traditional model-based and memory-based collaborative filtering, up to the most recent recommendation techniques based on deep learning. We have chosen the movie domain as an application scenario, and a version of the classic MovieLens as a dataset for training and testing our models.
Valeriani, D., Sansonetti, G., Micarelli, A. (2020). A Comparative Analysis of State-of-the-Art Recommendation Techniques in the Movie Domain. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.104-118). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-58811-3_8].
A Comparative Analysis of State-of-the-Art Recommendation Techniques in the Movie Domain
Sansonetti G.
;Micarelli A.
2020-01-01
Abstract
Recommender systems (RSs) represent one of the manifold applications in which Machine Learning can unfold its potential. Nowadays, most of the major online sites selling products and services provide users with RSs that can assist them in their online experience. In recent years, therefore, we have witnessed an impressive series of proposals for novel recommendation techniques that claim to ensure significative improvements compared to classic techniques. In this work, we analyze some of them from a theoretical and experimental point of view and verify whether they can deliver tangible real improvements in terms of performance. Among others, we have experimented with traditional model-based and memory-based collaborative filtering, up to the most recent recommendation techniques based on deep learning. We have chosen the movie domain as an application scenario, and a version of the classic MovieLens as a dataset for training and testing our models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
