A learnable search result diversification method

摘要

Search result diversification is to tackle the ambiguous queries and multi-faced information needs. The search result diversification problem can be formalized as a balance between the relevance score and the diversity score. Most previous diversification models utilize a predefined function to calculate the diversity score. The values of parameters need to be tuned by manual experiments. It is time-consuming and hard to reach optimal result in diversity evaluation. Proposing a learnable approach to solve the above problems is a pressing task. Therefore we introduce a Learnable Search Result Diversification model called L-SRD. On this basis, we redefine the diversity function and derive our loss function as the likelihood loss of ground truth generation. Stochastic gradient descent algorithm is employed to optimize the values of parameters. Finally we derive our ranking function to generate the diverse list sequentially. Due to the learning model, the values of parameters are determined automatically and get optimally. The experiments on TREC web tracks show that our approach outperforms several existing diversification models significantly. (C) 2018 Elsevier Ltd. All rights reserved.