Session-Aware Recommendation Algorithm

Context:
- It can be implemented by a Session-Aware Recommendation System (that solves a Session-Aware recommendation task).
Example(s):
- an RNN-based Session Aware Recommendation Algorithm.
See: Context-Aware Recommendation Algorithm.

References

(Quadrana et al., 2017) ⇒ Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. (2017). “Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks.” In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 130-137.
- ABSTRACT: Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs.

(Hidasi et al., 2016) ⇒ Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. (2016). “Session-based Recommendations with Recurrent Neural Networks.” In: ICLR-2016
- ABSTRACT: We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.