Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time
This work addresses session recommendation for users by proposing an incremental improvement to existing RNN methods.
The paper tackled session-based recommendation by incorporating item dwell time as an implicit measure of user interest into Recurrent Neural Networks, resulting in improved recommendation performance, though the authors note that minor gains require careful model selection with multiple validation sets.
Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.