ViTOR: Learning to Rank Webpages Based on Visual Features
This addresses the problem of webpage ranking for search engines by integrating visual information, but it is incremental as it builds on existing methods with a new dataset.
The paper tackles the problem of learning to rank webpages by incorporating visual features, introducing the ViTOR model and a new dataset, and shows that it significantly improves ranking performance.
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