The deep learning wave is propagating through many research areas and communities. In the last years it quickly propagated to Recommendation Systems, a research area which aims to recommend items to users. Indeed, many deep learning models and architectures have been proposed for Recommendation Systems to improve collaborative €ltering and content based algorithms. In this paper we propose a hybrid recommendation system combining user ratings and natural language text processing to solve the 0/1 recommendation problem. In particular, we describe a deep learning architecture combining two information sources, namely natural language text and user rating. Natural language text is used to learn a user-specific content-based classifier, while user ratings are used to develop user-Adaptive collaborative filtering recommendations. We perform numerical experiments on MovieLens 1M and reach first preliminary, but promising results, showing the proposed architecture has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor.

Sottocornola, G., Stella, F., Zanker, M. (2017). Towards a deep learning model for hybrid recommendation. In Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 (pp.1260-1264). Association for Computing Machinery, Inc [10.1145/3106426.3110321].

Towards a deep learning model for hybrid recommendation

STELLA, FABIO ANTONIO;
2017

Abstract

The deep learning wave is propagating through many research areas and communities. In the last years it quickly propagated to Recommendation Systems, a research area which aims to recommend items to users. Indeed, many deep learning models and architectures have been proposed for Recommendation Systems to improve collaborative €ltering and content based algorithms. In this paper we propose a hybrid recommendation system combining user ratings and natural language text processing to solve the 0/1 recommendation problem. In particular, we describe a deep learning architecture combining two information sources, namely natural language text and user rating. Natural language text is used to learn a user-specific content-based classifier, while user ratings are used to develop user-Adaptive collaborative filtering recommendations. We perform numerical experiments on MovieLens 1M and reach first preliminary, but promising results, showing the proposed architecture has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor.
paper
Recommendation Systems; Deep learning; hybrid recommendation system
English
IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 23-26 August
2017
Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
9781450349512
2017
1260
1264
none
Sottocornola, G., Stella, F., Zanker, M. (2017). Towards a deep learning model for hybrid recommendation. In Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 (pp.1260-1264). Association for Computing Machinery, Inc [10.1145/3106426.3110321].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/160736
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