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This paper explores the development of a framework for content-aware user profiling, studying how image producers and consumers can be better understood and consequently better served through services such as matchmaking and friend recommendations. User interests and similarities are extracted and analyzed on the edge employing state of the art CNN models over user images for the tasks of classification, as well as, building latent user representations from personal media content. A private-by-design approach is employed through the development and deployment of models on-device, avoiding the need for communicating personal data to a central service. Experimental results show that user profiling can provide accurate ranking of the users’ interests and meaningful user associations through profile similarity. |
