Enhancing topical ranking with preferences from click-through data

Yi Chang, Anlei Dong, Ciya Liao, Zhaohui Zheng

Abstract: To overcome the training data insufficiency problem for dedicated model in topical ranking, this paper proposes to utilize click-through data to improve learning. The efficacy of click-through data is explored under the framework of preference learning. The empirical experiment on a commercial search engine shows that, the model trained with the dedicated labeled data combined with skip-next preferences could beat the baseline model and the generic model in NDCG5 for 4.9% and 2.4% respectively.