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DEEP STACKING NETWORKS FOR INFORMATION RETRIEVAL
| Content Provider | CiteSeerX |
|---|---|
| Author | Deng, Li He, Xiaodong Gao, Jianfeng |
| Abstract | 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. Index Terms — deep stacking network, information retrieval, document ranking |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Scalable Learning Cumulative Gain High Ir-quality Region Dsn-based System Image Classification Task Optimization Method Recent State-of-the-art Flat Relationship Ir Quality Previous Dsn Method Document Ranking Mean Square Error Careful Regularization Method Special Type Deep Model Deep Stacking Network Information Retrieval Deep Stacking Network Index Term Wide Range Classification Rate Desirable Monotonic Correlation Sponsor Search Lambdarank-based System Successful Application Information Retrieval |
| Content Type | Text |
