Extended Features using Machine Learning Techniques for Photo Liking Prediction

Abstract

Today several photo platforms provide thousands of new pictures, it becomes ambitious to find highly appealing or like-able photos within such loads of data. Here, automatic liking prediction can support users in handling their pictures or improve ranking in sharing platforms. We describe a machine learning approach for photo liking prediction. Our features are based on various techniques, e.g. natural language processing/sentiment analysis, pre-trained deep learning networks, social network analysis and extended previously reported features. We conduct large-scale experiments using a collected dataset consisting of 80k photos based on two main categories from 500px with different settings. In our experiments we analyzed the impact of our newly features and found that social network features have the strongest influence for liking prediction, we achived a boost of 15%. Furthermore, we show that all implemented features are able to improve prediction accuracy of liking rates. We additionally analyze which groups of features that can be derived directly from pictures are usable for prediction.