  The visual appearance of a webpage carries valuable information about its
quality and can be used to improve the performance of learning to rank (LTR).
We introduce the Visual learning TO Rank (ViTOR) model that integrates
state-of-the-art visual features extraction methods by (i) transfer learning
from a pre-trained image classification model, and (ii) synthetic saliency heat
maps generated from webpage snapshots. Since there is currently no public
dataset for the task of LTR with visual features, we also introduce and release
the ViTOR dataset, containing visually rich and diverse webpages. The ViTOR
dataset consists of visual snapshots, non-visual features and relevance
judgments for ClueWeb12 webpages and TREC Web Track queries. We experiment with
the proposed ViTOR model on the ViTOR dataset and show that it significantly
improves the performance of LTR with visual features
