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Learning from Click Model and Latent Factor Model for Relevance Prediction Challenge

posted Mar 1, 2012, 10:07 AM by Botao Hu   [ updated Mar 4, 2012, 11:55 AM ]
Main Author and Experiment Conductor
Jun 2011 - Jul 2011
Coauthored with Nathan Liu, Weizhu Chen.
Supervised by Qiang Yang.
Hong Kong University of Science and Technology, Hong Kong
In Proceedings of WSCD 2012

We formally championed the Relevance Prediction Challenge with the cash prize and are invited to WSCD 2012

How to accurately interpret user click behavior in search log is a key but challenging problem for search relevance. In this paper, we describe our solution to the relevance prediction challenge which achieves the first place among eligible teams. There are three stages in our solution: feature generation, feature augmentation and learning a ranking function. In the first stage, we extract features in relation to query-document pairs as well as individual queries and documents from the click log data. In the second stage, we induce additional features by click model techniques and learning latent factor models to correct different biases and discover the correlations between different queries or documents respectively. In the final stage, we apply supervised learning models on the limited labelled data to induce a model for predicting relevance based on the features generated in the previous two stages.
Botao Hu,
May 4, 2012, 12:41 AM