Personalised Recommendations for Modes of Transport: A Sequence-based Approach

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Title: Personalised Recommendations for Modes of Transport: A Sequence-based Approach
Authors: Kumar, Gunjan
Jerbi, Houssem
O'Mahony, Michael P.
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Date: 14-Aug-2016
Abstract: In this paper we consider the problem of recommending modes of transport to users in an urban setting. In particular, we build on our past work in which a general framework for activity recommendation is proposed. To model the personal preferences and habits of users, the framework uses a sequence-based approach to capture the order as well as the context associated with user activity patterns. Here, we extend this work by introducing a machine learning approach to learn and take into account the natural variations in the regularity and repetition of individual user behaviour that occur. We demonstrate the versatility of our recommendation framework by applying it to the transport domain, and an evaluation using a real-world (mode of transport) dataset demonstrates the efficacy of the approach.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Copyright (published version): 2016 the Authors
Keywords: Recommender systems
Language: en
Status of Item: Peer reviewed
Conference Details: The 5th International Workshop on Urban Computing (UrbComp 2016), San Francisco, California, United States, 14 August 2016
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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