The ArXiv preprint to our paper introducing a joint Point process and Hierarchical RNN for item and time prediction is now available.

**Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions**

*In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several additions have been proposed for extending such models in order to handle specific problems or data. Two such extensions are 1.) modeling of inter-session relations for catching long term dependencies over user sessions, and 2.) modeling temporal aspects of user-item interactions. The former allows the session-based recommendation to utilize extended session history and inter-session information when providing new recommendations. The latter has been used to both provide state-of-the-art predictions for when the user will return to the service and also for improving recommendations. In this work we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction. The model consists of a Hierarchical RNN for the inter-session and intra-session items recommendation extended with a Point Process model for the time-gaps between the sessions. The experimental results indicate that the proposed model improves recommendations significantly on two datasets over a strong baseline, while simultaneously improving return-time predictions over a baseline return-time prediction model.*

Our paper

«Time is of the essence: A joint Hierarchical RNN and Point Process model for time and item predictions»has been accepted at 12th ACM International Conference on Web Search and Data Mining (WSDM). Collaborative work with Bjørnar Vassøy, Massimiliano Ruocco and Erlend Aune. WSDM is one of the top conferences in the domain of data mining, information retrieval and machine learning on the Web. This year WSDM had 511 submissions with an acceptance rate of 16%. Soon we will provide a link to the preprint and source-code.In this paper, we have proposed a joint model with a shared latent representation for a Point Process model (for time prediction) and a Hierarchical Recurrent Neural Network (HRNN). By doing so we are able to model a multi-session recommendation problem, together with returning time prediction.

This work was developed as part of the Norwegian Open AI Lab in cooperation with Telenor Research.

Looking forward to visiting Melbourne again in the summer!