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Discovery Projects - Grant ID: DP140102185

Title
Discovery Projects - Grant ID: DP140102185
Funding
ARC | Discovery Projects
Contract (GA) number
DP140102185
Start Date
2014/01/01
End Date
2016/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP140102185

 

  • Learning Points and Routes to Recommend Trajectories

    Chen, Dawei; Ong, Cheng Soon; Xie, Lexing (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP140102185 (DP140102185)
    The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Info...

    Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches

    Kim, Dongwoo; Xie, Lexing; Ong, Cheng Soon (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP140102185 (DP140102185)
    Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph ...

    PathRec: Visual Analysis of Travel Route Recommendations

    Chen, Dawei; Kim, Dongwoo; Xie, Lexing; Shin, Minjeong; Menon, Aditya Krishna; Ong, Cheng Soon; Avazpour, Iman; Grundy, John (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP140102185 (DP140102185)
    We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports d...
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  • Scientific Results

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    PUBLICATIONS BY ACCESS MODE

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