A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks
| Autor: | Pasquale Lops, Claudio Greco, Giovanni Semeraro, Alessandro Suglia, Marco de Gemmis, Cataldo Musto |
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| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning 02 engineering and technology Recommender system Machine learning computer.software_genre Session (web analytics) Recurrent neural network 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Word2vec Relevance (information retrieval) Artificial intelligence Data mining business computer |
| Zdroj: | UMAP |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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