A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems

Thomas, Sherin and Srijith, P K and Lukasik, M (2018) A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems. In: UMAP '18 Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, 8-11 July 2018, Singapore.

[img]
Preview
Text
ACM Digital Library_1-2_2018.pdf - Submitted Version

Download (653kB) | Preview

Abstract

In order to sustain the user-base for a web service, it is important to know the return time of a user to the service. We propose a Bayesian point process, log Gaussian Cox process (LGCP), to model and predict return time of users. It allows encoding the prior do- main knowledge and non-parametric estimation of latent intensity functions capturing user behaviour. We capture the similarities among the users in their return time by using a multi-task learning approach. We show the effectiveness of the proposed approaches on predicting the return time of users to last.fm music service.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Srijith, P KUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 06 Jul 2018 06:49
Last Modified: 06 Jul 2018 06:49
URI: http://raiith.iith.ac.in/id/eprint/4200
Publisher URL: https://doi.org/10.1145/3209219.3209261
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 4200 Statistics for this ePrint Item