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The visual appearance of a webpage carries valuable information about itsquality and can be used to improve the performance of learning to rank (LTR).We introduce the Visual learning TO Rank (ViTOR) model that integratesstate-of-the-art visual features extraction methods by (i) transfer learningfrom a pre-trained image classification model, and (ii) synthetic saliency heatmaps generated from webpage snapshots. Since there is currently no publicdataset for the task of LTR with visual features, we also introduce and releasethe ViTOR dataset, containing visually rich and diverse webpages. The ViTORdataset consists of visual snapshots, non-visual features and relevancejudgments for ClueWeb12 webpages and TREC Web Track queries. We experiment withthe proposed ViTOR model on the ViTOR dataset and show that it significantlyimproves the performance of LTR with visual features

ViTOR: Learning to Rank Webpages Based on Visual Features