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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Real-Time Session-Based Recommendations Using LSTM with Neural Embeddings
verfasst von : David Lenz, Christian Schulze, Michael Guckert
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018
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Abstract
Recurrent neural networks have successfully been used as core elements of intelligent recommendation engines in e-commerce platforms. We demonstrate how LSTM networks can be applied to recommend products of interest for a customer, based on the events of the current session only. Inspired by recent advances in natural language processing, our network computes vector space representations (VSR) of available products and uses these representations to derive predictions of user behaviour based on the clickstream of the current session. The experimental results suggest that the Embedding-LSTM is well suited for session-based recommendations, thus offering a promising method for attacking the user cold start problem. A live test gives proof that our LSTM model outperforms a recommendation model created with traditional methods. We also show that providing the learned VSR as features to neighbourhood-based methods leads to improved performance as compared to standard nearest neighbour methods.