Accelerated query processing via similarity score prediction
M Petri, A Moffat, J Mackenzie, J Shane Culpepper, D Beck
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval | ACM | Published : 2019
Publication rights licensed to ACM. Processing top-k bag-of-words queries is critical to many information retrieval applications, including web-scale search. In this work, we consider algorithmic properties associated with dynamic pruning mechanisms. Such algorithms maintain a score threshold (the k th highest similarity score identified so far) so that low-scoring documents can be bypassed, allowing fast top-k retrieval with no loss in effectiveness. In standard pruning algorithms the score threshold is initialized to the lowest possible value. To accelerate processing, we make use of term- and query-dependent features to predict the final value of that threshold, and then employ the predic..View full abstract
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Awarded by Australian Research Council
The third author was supported by an Australian Research Training Program Scholarship. The fifth author was suppported by the Australian Research Council (DP160102686).