Ranking with Auxiliary Data

摘要

Learning to rank arises in many information retrieval applications, ranging from Web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking function heavily depends on the number of labeled examples in the training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming. This presents a great need for making use of available auxiliary data, i.e., the within-domain unlabeled data and the out-of-domain labeled data. In this paper, we propose a general framework for ranking with auxiliary data, which is applicable to various ranking applications. Under this framework, we derive a generic ranking algorithm to effectively make use of both the within-domain unlabeled data and the out-of-domain labeled data. The proposed algorithm iteratively learns ranking functions for target domain and source domains and enforces their consensus on the unlabeled data in the target domain.