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SAGE: Interactive state-aware point-of-interest recommendation
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| Bernard Omidvar-Tehrani, Sruthi Viswanathan, Frédéric Roulland, Jean-Michel Renders |
| Workshop of State-based User Modelling, co-located with ACM International Conference on Web Search and Data Mining (WSDM) Conference, Houston Texas, USA, 4-7 February, 2020 |
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Point-of-Interest (POI) recommendation is surfacing in many location-based services. User models are employed in these services to leverage historical check-ins and social links, and enable personalized and socialized POI recommendations. However these models often lack interactivity (incorporating user interactions) and state-awareness. This deficiency aggravates in cold start situations, where nearly no user information (historical check-ins and social graph) is available to generate effective recommendations. In this paper, we propose SAGE, an interactive state-aware POI recommendation system which tackles the aforementioned challenges by exploiting look-alike groups mined in public POI datasets, such as Foursquare and Yelp. SAGE reformulates the problem of POI recommendation as recommending explainable look-alike groups (and their POIs) which are in line with user’s intent. SAGE frames the task of POI recommendation as an exploratory process where users interact with the system, and their interactions impact the way look-alike groups are picked out. Moreover, SAGE defines and employs mindsets which capture the actual state of the user and enforce the semantics of POI interestingness. Our experiments show that SAGE is an effective approach to capture interactivity and contextuality for recommending relevant look-alike groups and their POIs which are oriented towards the user’s mindset.
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The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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