Dietmar Jannach
Malte Ludewig
ABSTRACT
Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem of making session-based recommendations, i.e., for recommending the next item in an anonymous session, Hidasi et al.~recently investigated the application of recurrent neural networks with Gated Recurrent Units (GRU4REC). Assessing the true effectiveness of such novel approaches based only on what is reported in the literature is however difficult when no standard evaluation protocols are applied and when the strength of the baselines used in the performance comparison is not clear. In this work we show based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets. Neighborhood sampling and efficient in-memory data structures ensure the scalability of the kNN method. The best results in the end were often achieved when we combine the kNN approach with GRU4REC, which shows that RNNs can leverage sequential signals in the data that cannot be detected by the co-occurrence-based kNN method.
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Index Terms
- When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
Recommendations
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsSession-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 ...
Inter-Session Modeling for Session-Based Recommendation
DLRS 2017: Proceedings of the 2nd Workshop on Deep Learning for Recommender SystemsIn recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of ...
Context-aware session recommendation based on recurrent neural networks
AbstractA session-based recommendation system that helps users get the information they are interested in is an important category of personalized recommendation systems. Traditionally, session recommendation algorithms do not take full ...
Graphical abstractDisplay Omitted
Highlights- Embedding the session information to a low-dimensional RNN with hidden states of gated recurrent units.
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