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.

我们提出了一种基于循环神经网络 (RNN) 的会话推荐方法，考虑到实际问题和数据缺乏的情况，并引入了一些修改，如排序损失函数。实验结果表明，与广泛使用的方法相比，我们的方法在两个数据集上都有明显的改进。