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Kumar, Gunjan
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Kumar, Gunjan
Official Name
Kumar, Gunjan
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Now showing 1 - 3 of 3
- PublicationPersonalised Recommendations for Modes of Transport: A Sequence-based Approach(2016-08-14)
; ; 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.368 - PublicationTowards the Recommendation of Personalised Activity Sequences in the Tourism DomainIn this paper we consider the problem of recommending sequencesof activities to a user. The proposed approach leverages the order aswell as the context associated with the users past activity patternsto make recommendations. This work extends the general activityrecommendation framework proposed in [16] to iteratively recommendthe next sequence of activities to perform. We demonstratethe efficacy of our recommendation framework by applying it to thetourism domain and evaluations are performed using a real-world(checkin) dataset
265 - PublicationTowards Activity Recommendation from LifelogsWith the increasing availability of passive, wearable sensor devices, digital lifelogs can now be captured for individuals. Lifelogs contain a digital trace of a person’s life, and are characterised by large quantities of rich contextual data. In this paper, we propose a content based recommender system to leverage such lifelogs to suggest activities to users. We model lifelogs as timelines of chronological sequences of activity objects, and describe a recommendation framework in which a two-level distance metric is proposed to measure the similarity between current and past timelines. An initial evaluation of our activity recommender performed using a real-world lifelog dataset demonstrates the utility of our approach.
335Scopus© Citations 9