Learning For Search Result Diversification

Search result diversification has gained attention as a way to tackle the ambiguous or multi-faceted information needs of users. Most existing methods on this problem utilize a heuristic predefined ranking function, where limited features can be incorporated and extensive tuning is required for different settings. In this paper, we address search result diversification as a learning problem, and introduce a novel relational learning-to-rank approach to formulate the task. However, the definitions of ranking function and loss function for the diversification problem are challenging. In our work, we firstly show that diverse ranking is in general a sequential selection process from both empirical and theoretical aspects. On this basis, we define ranking function as the combination of relevance score and diversity score between the current document and those previously selected, and loss function as the likelihood loss of ground truth based on Plackett-Luce model, which can naturally model the sequential generation of a diverse ranking list. Stochastic gradient descent is then employed to conduct the unconstrained optimization, and the prediction of a diverse ranking list is provided by a sequential selection process based on the learned ranking function. The experimental results on the public TREC datasets demonstrate the effectiveness and robustness of our approach.