Search task success evaluation by exploiting multi-view active semi-supervised learning

•Propose MA4SE that exploits labeled data and unlabeled data by integrating the advantages of both semi-supervised learning and active learning with the multi-view mechanism.•Design an integrated selection strategy to measure the informativeness and the representativeness of contention search tasks.•Conduct extensive experiments on open datasets to show that the proposed approach outperforms the state-of-the-art semi-supervised search task success evaluation approach.