Abstract
Sruthi Viswanathan, Bernard Omidvar-Tehrani, Adrien Bruyat, Frédéric Roulland, Antonietta Grasso |
Computer Human Interaction conference (CHI), Honolulu, Hawaii, USA, 25 April-1 May, 2020 |
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Abstract
In this paper we report the results from a Hybrid Wizard of Oz study consisting of a critical incident interview and an in-situ simulation. The study was aimed at validating the need for a contextualised and personalised Point-Of-Interest (POI) recommender and understanding the detailed user needs for it. Our key findings include: feeling bored as a key trigger to search for POIs, trust issues with the existing recommendation sources, intent to find free activities, information needs on areas-of-interest beyond points-of-interest, socialising support, and language barriers. With this study we also exemplify a cost and time effective approach for design of intelligent systems.
1. Difference in female/male salary: 33/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: uncalculable
4. Number of employees in under-represented gender in 10 highest salaries: 0/10 points
NAVER France targets (with respect to the 2022 index) are as follows:
En 2022, NAVER France a obtenu les notes suivantes pour chacun des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 33 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : non calculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 0 sur 10 points
Les objectifs de progression pour l’index 2022 de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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