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

Thomas, Sherin and Srijith, P K (2018) A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems. Masters thesis, Indian Institute of Technology Hyderabad.

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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. In this work, we propose a point process model which captures the temporal dynamics of the user activities associated with a web service. The time at which the user returns to the service is predicted, given a set of historical data. We propose to use a Bayesian non-parametric model, log Gaussian Cox process (LGCP), which allows the latent intensity function generating the return times to be learnt non-parametrically from the data. It also allows us to encode prior domain knowledge such as periodicity in users return time using Gaussian process kernels. Further, we cap- ture the similarities among the users in their return time by using a multi-task learning approach in the LGCP framework. We compare the performance of LGCP with different kernels on a real- world last.fm data and show their superior performance over standard radial basis function kernel and baseline models. We also found LGCP with multitask learning kernel to provide an improved predictive performance by capturing the user similarity.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (Masters)
Uncontrolled Keywords: Return Time Prediction, Log Gaussian Cox Process, Recommendation System, Multi Task Learning
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 27 Jun 2018 06:28
Last Modified: 06 Jul 2018 06:23
URI: http://raiith.iith.ac.in/id/eprint/4084
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