Schema-summarization in linked-data-based feature selection for recommender systems

By Azzurra Ragone, Paolo Tomeo, Corrado Magarelli, Tommaso Di Noia, M. Palmonari, Andrea Maurino, Eugenio Di Sciascio. 2017

24 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Azzurra Ragone
2. Vito Bellini
3. Angelo Schiavone
4. Eugenio Di Sciascio
5. Tommaso Di Noia
6. Vito Walter Anelli

In Information Systems

1. Azzurra Ragone
2. Vito Bellini
3. Angelo Schiavone
4. Eugenio Di Sciascio
5. Tommaso Di Noia
6. Vito Walter Anelli

In Signal Processing

1. Tommaso Di Noia
2. Eugenio Di Sciascio
3. Vito Bellini

Citations in the corpus

• Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, methodology): “In our experimental setting aimed at evaluating the explainability of the trained model, while building the structure of the SemAuto autoencoder we used those KG entities reachable through the predicate dbo:subject as item categories, while we used the approach originally proposed in [13] to select ”
• Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios (2017, background): “To the best of our knowledge, the only works dealing with automated feature selection from knowledge graphs are [16, 22].”