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A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER FOR HOTEL RATING PREDICTION

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Citation Author(s):
Myrsini Demi, Constantine Kotropoulos, Emmanouil Gionanidis
Submitted by:
C Kotropoulos
Last updated:
4 November 2019 - 10:42am
Document Type:
Presentation Slides
Document Year:
2019
Event:
Presenters Name:
Constantine Kotropoulos
Paper Code:
MLSP2019-L1.4

Abstract 

Abstract: 

A successful recommender system interacts with users and learns their preferences. This is crucial in order to provide accurate recommendations. In this paper, a Weighted Ordered Probit Collaborative Kalman filter is proposed for hotel rating prediction. Since potential changes may occur in hotel services or accommodation conditions, a hotel popularity may be volatile through time. A weighted ordered probit model is introduced to capture this latent trend about each hotel popularity through time. It is demonstrated by experiments that such model of hotel popularity trends reinforces the performance of Collaborative Kalman filter, yielding more accurate potential recommendations.

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A WEIGHTED ORDERED PROBIT COLLABORATIVE KALMAN FILTER

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