Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Automatic feature selection in the markov random field model for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Early exit optimizations for additive machine learned ranking systems
Proceedings of the third ACM international conference on Web search and data mining
Efficient set intersection for inverted indexing
ACM Transactions on Information Systems (TOIS)
Bagging gradient-boosted trees for high precision, low variance ranking models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Posting list intersection on multicore architectures
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Faster top-k document retrieval using block-max indexes
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to Rank for Information Retrieval and Natural Language Processing
To index or not to index: time-space trade-offs in search engines with positional ranking functions
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient and effective retrieval using selective pruning
Proceedings of the sixth ACM international conference on Web search and data mining
Evaluation as a service for information retrieval
ACM SIGIR Forum
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This paper examines a multi-stage retrieval architecture consisting of a candidate generation stage, a feature extraction stage, and a reranking stage using machine-learned models. Given a fixed set of features and a learning-to-rank model, we explore effectiveness/efficiency tradeoffs with three candidate generation approaches: postings intersection with SvS, conjunctive query evaluation with WAND, and disjunctive query evaluation with WAND. We find no significant differences in end-to-end effectiveness as measured by NDCG between conjunctive and disjunctive WAND, but conjunctive query evaluation is substantially faster. Postings intersection with SvS, while fast, yields substantially lower end-to-end effectiveness, suggesting that document and term frequencies remain important in the initial ranking stage. These findings show that conjunctive WAND is the best overall candidate generation strategy of those we examined.