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  1. Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems (DLRS 2017)
  2. Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios
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Keynote: Bayesian Deep Learning Models for Recommendation Applications
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Specializing Joint Representations for the task of Product Recommendation
Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios
Towards Recommender Systems for Police Photo Lineup
Inter-Session Modeling for Session-Based Recommendation
A Deep Multimodal Approach for Cold-start Music Recommendation
Recurrent Latent Variable Networks for Session-Based Recommendation
Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set Songs

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Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

Content Provider ACM Digital Library
Author Di Sciascio, Eugenio Bellini, Vito Di Noia, Tommaso Anelli, Vito Walter
Abstract In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments, we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weight semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behaves in the presence of a few ratings from a user on the Movielens 1M dataset and compare results with BPRSLIM.
Starting Page 60
Ending Page 66
Page Count 7
File Format PDF
ISBN 9781450353533
DOI 10.1145/3125486.3125496
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-08-27
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Deep learning Autoencoders Dbpedia Knowledge graphs Linked open data Recommender systems
Content Type Text
Resource Type Article
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