Deep stacking networks for information retrieval

Deep stacking networks (DSN) are a special type of deep model equipped with parallel and scalable learning. We report successful applications of DSN to an information retrieval (IR) task pertaining to relevance prediction for sponsor search after careful regularization methods are incorporated to the previous DSN methods developed for speech and image classification tasks. The DSN-based system significantly outperforms the LambdaRank-based system which represents a recent state-of-the-art for IR in normalized discounted cumulative gain (NDCG) measures, despite the use of mean square error as DSN's training objective. We demonstrate desirable monotonic correlation between NDCG and classification rate in a wide range of IR quality. The weaker correlation and more flat relationship in the high IR-quality region suggest the need for developing new learning objectives and optimization methods.