  Image feature representation plays an essential role in image recognition and
related tasks. The current state-of-the-art feature learning paradigm is
supervised learning from labeled data. However, this paradigm requires
large-scale category labels, which limits its applicability to domains where
labels are hard to obtain. In this paper, we propose a new data-driven feature
learning paradigm which does not rely on category labels. Instead, we learn
from user behavior data collected on social media. Concretely, we use the image
relationship discovered in the latent space from the user behavior data to
guide the image feature learning. We collect a large-scale image and user
behavior dataset from Behance.net. The dataset consists of 1.9 million images
and over 300 million view records from 1.9 million users. We validate our
feature learning paradigm on this dataset and find that the learned feature
significantly outperforms the state-of-the-art image features in learning
better image similarities. We also show that the learned feature performs
competitively on various recognition benchmarks.
