Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

Manotumruksa, J. (2017) Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation. In: SIGIR 2017: The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan, 7-11 Aug 2017, p. 1383. ISBN 9781450350228 (doi: 10.1145/3077136.3084159)

Abstract

In recent years, vast amounts of user-generated data have being created on Location-Based Social Networks (LBSNs) such as Yelp and Foursquare. Making effective personalised venue suggestions to users based on their preferences and surrounding context is a challenging task. Context-Aware Venue Recommendation (CAVR) is an emerging topic that has gained a lot of attention from researchers, where context can be the user's current location for example. Matrix Factorisation (MF) is one of the most popular collaborative filtering-based techniques, which can be used to predict a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feedback is easier to obtain from LBSNs as it does not require the users to explicitly express their satisfaction with the venues. In addition, the MF-based approaches usually suffer from the sparsity problem where users/venues have very few rating, hindering the prediction accuracy. Although previous works on user-venue rating prediction have proposed to alleviate the sparsity problem by leveraging user-generated data such as social information from LBSNs, research that investigates the usefulness of Deep Neural Network algorithms (DNN) in alleviating the sparsity problem for CAVR remains untouched or partially studied.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Manotumruksa, Mr Jarana
• Authors: Manotumruksa, J.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450350228
• Copyright Holders: Copyright © 2017 ACM
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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