Paper Info
Title
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions.
Abstract
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.
Year
DOI
Venue
2019
10.1145/3289600.3290987
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
Keywords
DocType
Volume
point processes, recommender systems, recurrent neural network, session-based recommendation
Conference
abs/1812.01276
Citations 
PageRank 
References 
2
0.37
19
Authors
4
Name
Order
Citations
PageRank
Bjørnar Vassøy120.37
Massimiliano Ruocco251.10
Eliezer de Souza da Silva351.09
Erlend Aune420.37