Regularising Factorised Models for Venue Recommendation using Friends and their Comments

Manotumruksa, J., Macdonald, C. and Ounis, I. (2016) Regularising Factorised Models for Venue Recommendation using Friends and their Comments. In: CIKM 2016: 25th ACM International Conference on Information and Knowledge Management, Indianapolis, United States, 24-28 Oct 2016, ISBN 9781450340731 (doi:10.1145/2983323.2983889)

Manotumruksa, J., Macdonald, C. and Ounis, I. (2016) Regularising Factorised Models for Venue Recommendation using Friends and their Comments. In: CIKM 2016: 25th ACM International Conference on Information and Knowledge Management, Indianapolis, United States, 24-28 Oct 2016, ISBN 9781450340731 (doi:10.1145/2983323.2983889)

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Abstract

Venue recommendation is an important capability of Location-Based Social Networks such as Yelp and Foursquare. Matrix Factorisation (MF) is a collaborative filtering-based approach that can effectively recommend venues that are relevant to the users' preferences, by training upon either implicit or explicit feedbacks (e.g. check-ins or venue ratings) that these users express about venues. However, MF suffers in that users may only have rated very few venues. To alleviate this problem, recent literature have leveraged additional sources of evidence, e.g. using users' social friendships to reduce the complexity of - or regularise - the MF model, or identifying similar venues based on their comments. This paper argues for a combined regularisation model, where the venues suggested for a user are influenced by friends with similar tastes (as defined by their comments). We propose a MF regularisation technique that seamlessly incorporates both social network information and textual comments, by exploiting word embeddings to estimate a semantic similarity of friends based on their explicit textual feedback, to regularise the complexity of the factorised model. Experiments on a large existing dataset demonstrate that our proposed regularisation model is promising, and can enhance the prediction accuracy of several state-of-the-art matrix factorisation-based approaches.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Manotumruksa, Mr Jarana
Authors: Manotumruksa, J., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450340731
Copyright Holders:Copyright © 2016 ACM
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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