Computing Recommendations via a Knowledge Graph-aware Autoencoder

By Vito Bellini, Angelo Schiavone, Tommaso Di Noia, Azzurra Ragone, Eugenio Di Sciascio. 2018

8 citations recorded by the source corpus.

In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields. Among others, the use of deep learning for the recommendation problem, although new, looks quite promising due to its positive performances in terms of accuracy of recommendation results. In a recommendation setting, in order to predict user ratings on unknown items a possible configuration of a deep neural network is that of autoencoders tipically used to produce a lower dimensionality representation of the original data. In this paper we present KG-AUTOENCODER, an autoencoder that bases the structure of its neural network on the semanticsaware topology of a knowledge graph thus providing a label for neurons in the hidden layer that are eventually used to build a user profile and then compute recommendations. We show the effectiveness of KG-AUTOENCODER in terms of accuracy, diversity and novelty by comparing with state of the art recommendation algorithms.

How researchers see this paper

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

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

Citations in the corpus

• Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, methodology): “In [3] we prove that SemAuto can also be effectively used to compute recommendations in non-cold situations reaching very competitive results in terms of accuracy and diversity.”