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Notes on Session-Based Recommendations With Recurrent Neural Networks



This paper presents a Recommender System based on Recurrent Neural Networks. In particular, the authors focus on recommendations based on session data, which are useful when recommending to anonymous users or for fast changing items (e.g. classified sites). The algorithm currently used for the problem is Item-KNN, which takes into account only the last event of the session; RNNs should have an advantage, factoring in the whole session.

My two cents

While at RecSys ’15, I was really really puzzled by the absence of Deep Learning papers. Finally a promising research that can add new algorithms to the toolbelt! It’s also interesting that the paper was submitted to ICLR.

CEO and Chief Data Scientist at Optimist AI, bringing innovation to sales through Big Data. The future is bright.

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