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.
This work was partially funded by LOEWE HA project PAROT (no. 509/16-21, State Offensive for the Development of Scientific and Economic Excellence).
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Notes
- 1.
This is dependent on the display size. Here we assume a 24 in. monitor.
- 2.
From a marketing perspective, an interesting metric is the revenue/click. However, this neglects the cost of running the systems which is indeed high so that only looking at the revenue/click does not incorporate all relevant costs and is therefore only a skewed metric. Unfortunately, we cannot publish details about the associated cost structures.
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Lenz, D., Schulze, C., Guckert, M. (2018). Real-Time Session-Based Recommendations Using LSTM with Neural Embeddings. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_33
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