Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation

Manotumruksa, J., Macdonald, C. and Ounis, I. (2018) Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation. In: 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 8-12 Jul 2018, pp. 555-564. ISBN 9781450356572 (doi: 10.1145/3209978.3210042)

160908.pdf - Accepted Version



Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Macdonald, Professor 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
Copyright Holders:Copyright © 2018 Association for Computing Machinery
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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