User Preference Prediction in Mobile Search

Abstract

As search requests from mobile devices are growing very quickly, mobile search evaluation becomes one of the central concerns in mobile search studies. Beyond traditional Cranfield paradigm, side-by-side user preference between two ranked lists does not rely on user behavior assumptions and has been shown to produce more accurate results comparing to traditional evaluation methods based on “query-document” relevance. On the other hand, result list preference judgements have very high annotation cost. Previous studies attempted to assist human judges by automatically predicting preference. However, whether these models are effective in mobile search environment is still under investigation. In this paper, we proposed a machine learning model to predict user preference automatically in mobile search environment. We find that the relevance features can predict user preference very well, so we compare the agreement of evaluation metrics with side-by-side user preferences on our dataset. We get inspiration from the agreement comparison method and proposed new relevance features to build models. Experimental results show that our proposed model can predict user preference very effectively.

This work is supported by Natural Science Foundation of China (Grant No. 61622208, 61532011, 61672311) and National Key Basic Research Program (2015CB358700).