Sequential User-based Recurrent Neural Network Recommendations

Donkers, T., Loepp, B., and Ziegler, J. (2017). Proceedings of the Eleventh ACM Conference on Recommender Systems, 152–160. New York: ACM.


Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.

Related focus areas


Related publications

On User Awareness in Model-Based Collaborative Filtering Systems

Loepp, B. and Ziegler, J. (2017). Proceedings of the 1st Workshop on Awareness Interfaces and Interactions.

Interactive Recommending: Framework, State of Research and Future Challenges

Loepp, B., Barbu, C.-M., and Ziegler, J. (2016). Proceedings of the Workshop on Engineering Computer-Human Interaction in Recommender Systems, 1705, 3–13.

Towards Understanding Latent Factors and User Profiles by Enhancing Matrix Factorization with Tags

Donkers, T., Loepp, B., and Ziegler, J. (2016). Poster Proceedings of the 10th ACM Conference on Recommender Systems (RecSys 2016): Boston, USA, September 17, 2016, 1688.