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Traditional learning to rank methods learn ranking models from training data in a batch and offline learning mode, which suffers from some critical limitations, e.g., poor scalability as the model has to be retrained from scratch whenever new training data arrives. This is clearly nonscalable for many real applications in practice where training data often arrives sequentially and frequently. To overcome the limitations, this paper presents SO-LAR -a new framework of Scalable Online Learningdoi:10.3115/v1/p15-1163 dblp:conf/acl/WangWZH15 fatcat:vx62h4zrijcbvb6bucqn2m73fq