Some like it hot - visual guidance for preference prediction
Rasmus Rothe, Radu Timofte, Luc Van Gool

TL;DR
This paper presents a novel computational approach combining deep learning and visual regularized collaborative filtering to predict individual preferences for faces and images, achieving high accuracy and sociological insights.
Contribution
It introduces visual regularized collaborative filtering and a new regression method for visual queries without ratings, advancing preference prediction techniques.
Findings
Predicts 76% of ratings accurately from images alone
Validates methods on dating site data and celebrity images
Demonstrates effectiveness on movie poster dataset
Abstract
For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the state-of-the-art, but also predict - based on someone's known preferences - how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very…
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Videos
Some Like It Hot - Visual Guidance for Preference Prediction· youtube
Taxonomy
TopicsEvolutionary Psychology and Human Behavior · Demographic Trends and Gender Preferences · Media, Gender, and Advertising
