A Multimodal Learning to Rank model for Web Pages

Nikhila T Bhuvan,M Sudheep Elayidom,Division Of Computer Science, School Of Engineering, Cusat, Kerala, India

doi:10.35940/ijeat.f1442.089620

Abstract

“Learning-to-rank” or LTR utilizes machine learning technologies to optimally combine many features to solve the problem of ranking. Web search is one of the prominent applications of LTR. To improve the ranking of webpages, multimodality based Learning to Rank model is proposed and implemented. Multimodality is the fusion or the process of integrating multiple unimodal representations into one compact representation. The main problem with the web search is that the links that appear on the top of the search list may be either irrelevant or less relevant to the user than the one appearing at a lower rank. Researches have proven that a multimodality based search would improve the rank list populated. The multiple modalities considered here are the text on a webpage as well as the images on a webpage. The textual features of the webpages are extracted from the LETOR dataset and the image features of the webpages are extracted from the images inside the webpages using the concept of transfer learning. VGG-16 model, pre-trained on ImageNet is used as the image feature extractor. The baseline model which is trained only using textual features is compared against the multimodal LTR. The multimodal LTR which integrates the visual and textual features shows an improvement of 10-15% in web search accuracy.

Highlights

Learning to rank algorithm is a machine learning algorithm that ranks the documents automatically using an extracted feature set [4]
The Visual learning TO Rank (ViTOR) model significantly improved the performance of learning to rank (LTR) with visual features
Given is the sample table of queries of TREC web track and the number of training samples on which the model was trained on. These QIDs matches the QIDs in the LETOR dataset

Summary

Introduction

Learning to rank algorithm is a machine learning algorithm that ranks the documents automatically using an extracted feature set [4]. Learning-to-rank algorithms optimally combine features exacted from query-document pairs through discriminative training. It even can be used for rank aggregation like in the case of metasearch engines. Learning to rank becomes very useful in the case of search engines as daily they get a huge lot of training data in the form of user feedbacks and search logs. This can constantly improve their ranking mechanism. A part time doctoral student in School of Engineering, Cochin University of Science and Technology(CUSAT), Kochi, India She completed her BTech from the Kannur University and MTech from IGNOU. Her areas of Interest are Cloud Computing, Information Retrieval, and Machine Learning

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