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.

Session-based Recommendations with Recurrent Neural Networks

This chapter presents an introduction to recurrent neural networks for readers familiar with artificial neural networks in general, and multi-layer perceptrons trained with gradient descent algorithms (back-propagation) in particular. A recurrent neural network (RNN) is an artificial neural network with internal loops. These internal loops induce recursive dynamics in the networks and thus introduce delayed activation dependencies across the processing elements (PEs) in the network.

Recurrent Neural Networks

https://scholar.uwindsor.ca/cgi/viewcontent.cgi?article=1064&context=odettepub

An approach to multiattribute decision making with interval-valued intuitionistic fuzzy assessments and incomplete weights

https://repository.up.ac.za/bitstream/handle/2263/16613/Mohandes_Estimation(2011).pdf?sequence=1

Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)

Some refinements of rough k-means clustering

Soft Computing and Tools of Intelligent Systems Design: Theory and Applications

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Soft Computing and Tools of Intelligent Systems Design: Theory and Applications