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 . 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.
 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
 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
 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)
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.