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
@article{arxiv.1903.02939,
title = {ViTOR: Learning to Rank Webpages Based on Visual Features},
author = {Bram van den Akker and Ilya Markov and Maarten de Rijke},
journal= {arXiv preprint arXiv:1903.02939},
year = {2019}
}
Comments
In Proceedings of the 2019 World Wide Web Conference (WWW 2019), May 2019, San Francisco