A deep architecture for content-based recommendations exploiting recurrent neural networks

Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco De Gemmis, Pasquale Lops, Giovanni Semeraro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Citations (Scopus)

Abstract

In this paper we investigate the effectiveness of Recurrent Neural Networks (RNNs) in a top-N content-based recommendation scenario. Specifically, we propose a deep architecture which adopts Long Short Term Memory (LSTM) networks to jointly learn two embeddings representing the items to be recommended as well as the preferences of the user. Next, given such a representation, a logistic regression layer calculates the relevance score of each item for a specific user and we returns the top-N items as recommendations. In the experimental session we evaluated the effectiveness of our approach against several baselines: first, we compared it to other shallow models based on neural networks (as Word2Vec and Doc2Vec), next we evaluated it against state-of-The-Art algorithms for collaborative filtering. In both cases, our methodology obtains a significant improvement over all the baselines, thus giving evidence of the effectiveness of deep learning techniques in content-based recommendation scenarios and paving the way for several future research directions.

Original languageEnglish
Title of host publicationProceedings of the 25th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery
Pages202-211
Number of pages10
ISBN (Electronic)9781450346351
DOIs
Publication statusPublished - 9 Jul 2017
Event25th ACM International Conference on User Modeling, Adaptation, and Personalization 2017 - Bratislava, Slovakia
Duration: 9 Jul 201712 Jul 2017

Conference

Conference25th ACM International Conference on User Modeling, Adaptation, and Personalization 2017
Abbreviated titleUMAP 2017
Country/TerritorySlovakia
CityBratislava
Period9/07/1712/07/17

Keywords

  • Content representation
  • Deep learning
  • Recommender systems
  • Recurrent neural networks

ASJC Scopus subject areas

  • Software

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