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

How to accurately interpret user click behaviour 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 querydocument 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.