Neural-based Completion of a Global Point-of-Interest Database - Naver Labs Europe
preloder
NAVER LABS Europe
    Published
    30 August 2019
    Location
    Meylan, France
    Job Type
    Start date
    February 2019 - earlier starting date is also possible.
    Duration
    5-6 months

    Description

    Applications that process Point-of-Interest (POI) data, such as recommender systems, trip planners, or artificial intelligence (AI) based personal assistants, are omni-present nowadays. However, the success of such applications critically depends on the quality of the ingested data, and most importantly the completeness of supporting databases [1, 2]. Existing work on automatic data completion approaches has only recently partially considered the task for Points-of-Interest [3]. Such entities have a number of distinctive properties - notably multiscript names, geo-spatial identity, and temporally defined context -, which make the task more challenging.

     

    The successful candidate will work on neural-based data completion techniques, applied to a global database of Points-of-Interest. Possible axes of work include identifying appropriate representations for spatial and temporal information, exploring the effect of multi-script and multi-language information, and experimenting with corresponding models.

     

    [1] Felix Biessmann, David Salinas, Sebastian Schelter, Philipp Schmidt, and Dustin Lange. 2018. "Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18). ACM, New York, NY, USA, 2017–2025. https://doi.org/10.1145/3269206.3272005

     

    [2] Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich. 2018. Data Lifecycle Challenges in Production Machine Learning: A Survey. SIGMOD Rec. 47, 2 (Dec. 2018), 17–28. https://doi.org/10.1145/3299887.3299891

     

    [3] Hao Liu, Yongxin Tong, Panpan Zhang, Xinjiang Lu, Jianguo Duan and Hui Xiong. Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System. The 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2019), Anchorage, Alaska, USA, 2019. (Applied Data Science Track)

    Required skills

    The successful candidate should be enrolled in a graduate program, at the Master or PhD level, with a focus on Data Mining and/or Data Science.

    Strong programming skills and knowledge of one of the major deep learning toolkits (TensorFlow, PyTorch, Keras) are required.

    Publications in major international conferences will be strongly encouraged.

    Application instructions

    Please note that applicants must be registered students at a university or other academic institution and that this establishment will need to sign an 'Internship Convention' with NAVER LABS Europe before the student is accepted.

    You can apply for this position online. Don't forget to upload your CV and cover letter before you submit. Incomplete applications will not be accepted.

    About NAVER LABS

    NAVER LABS is a world class team of self-motivated and highly engaged researchers, engineers and interface designers collaborating together to create next generation ambient intelligence technology and services that are rich with the organic understanding they have of users, their contexts and situations.

    Since 2013 LABS has led NAVER’s innovation in technology through products such as the AI-based translation app ‘Papago’, the omni-tasking web browser ‘Whale’, the virtual AI assistant ‘WAVE’, in-vehicle information entertainment system ‘AWAY’ and M1, the 3D indoor mapping robot.

    The team in Europe is multidisciplinary and extremely multicultural specializing in artificial intelligence, machine learning, computer vision, natural language processing, UX and ethnography. We collaborate with many partners in the European scientific community on R&D projects.

    NAVER LABS Europe is located in the south east of France in Grenoble. The notoriety of Grenoble comes from its exceptional natural environment and scientific ecosystem with 21,000 jobs in public and private research. It is home to 1 of the 4 French national institutes in AI called MIAI (Multidisciplinary Innovation in Ai) It has a large student community (over 62,000 students) and is a lively and cosmopolitan place, offering a host of leisure opportunities. Grenoble is close to both the Swiss and Italian borders and is the ideal place for skiing, hiking, climbing, hang gliding and all types of mountain sports.

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