Computing recommendations via a Knowledge Graph-aware Autoencoder
This addresses the recommendation problem for users by improving accuracy, diversity, and novelty, but it appears incremental as it builds on existing autoencoder methods with a knowledge graph integration.
The paper tackles the recommendation problem by introducing KG-AUTOENCODER, an autoencoder that uses a knowledge graph to structure its neural network for building user profiles and computing recommendations, showing effectiveness in accuracy, diversity, and novelty compared to state-of-the-art algorithms.
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.