Matrix Factorisation with Word Embeddings for Rating Prediction on Location-Based Social Networks

Manotumruksa, J., Macdonald, C. and Ounis, I. (2017) Matrix Factorisation with Word Embeddings for Rating Prediction on Location-Based Social Networks. In: 39th European Conference on Information Retrieval, Aberdeen, Scotland, 8-13 April 2017, pp. 647-654. (doi:10.1007/978-3-319-56608-5_61)

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Abstract

With vast amounts of data being created on location-based social networks (LBSNs) such as Yelp and Foursquare, making effective personalised suggestions to users is an essential functionality. Matrix Factorisation (MF) is a collaborative filtering-based approach that is widely used to generate suggestions relevant to user’s preferences. In this paper, we address the problem of predicting the rating that users give to venues they visit. Previous works have proposed MF-based approaches that consider auxiliary information (e.g. social information and users’ comments on venues) to improve the accuracy of rating predictions. Such approaches leverage the users’ friends’ preferences, extracted from either ratings or comments, to regularise the complexity of MF-based models and to avoid over-fitting. However, social information may not be available, e.g. due to privacy concerns. To overcome this limitation, in this paper, we propose a novel MF-based approach that exploits word embeddings to effectively model users’ preferences and the characteristics of venues from the textual content of comments left by users, regardless of their relationship. Experiments conducted on a large dataset of LBSN ratings demonstrate the effectiveness of our proposed approach compared to various state-of-the-art rating prediction approaches.

Item Type:Conference Proceedings
Additional Information:Published in Lecture Notes in Computer Science, v. 10193, pp. 647-654
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Manotumruksa, Mr Jarana and Ounis, Professor Iadh
Authors: Manotumruksa, J., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
ISSN:0302-974
Published Online:08 April 2017
Copyright Holders:Copyright © 2017 Springer International Publishing AG
First Published:First published in Lecture Notes in Computer Science 10193:647-654
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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