Towards Activity Recommendation from Lifelogs
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|Title:||Towards Activity Recommendation from Lifelogs||Authors:||Kumar, Gunjan
O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/8455||Date:||6-Dec-2014||Abstract:||With 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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACM||Copyright (published version):||2014 ACM||Keywords:||Recommender systems; Lifelogging; Activity recommendation; Activity timeline matching||DOI:||10.1145/2684200.2684298||Language:||en||Status of Item:||Peer reviewed||Conference Details:||iiWAS '14: The 16th International Conference on Information Integration and Web-based Applications & Services (iiWAS2014), Hanoi, Vietnam, 4-6 December 2014|
|Appears in Collections:||Computer Science Research Collection|
Insight Research Collection
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