ABSTRACT
This paper investigates the influence of pruning feature lists to keep a given budget for the evaluation of ranking methods. We learn from a given training set how important the individual prefixes are for the ranking quality. Based on there importance we choose the best prefixes to calculate the ranking while keeping the budget.
- Michael Bendersky, Donald Metzler, and W. Bruce Croft. Learning concept importance using a weighted dependence model. In WSDM, pages 31--40, 2010. Google ScholarDigital Library
- Ihab F. Ilyas et al. A survey of top-query processing techniques in relational database systems. ACM Comput. Surv., 40(4), 2008. Google ScholarDigital Library
- Michal Shmueli-Scheuer et al. Best-effort top-k query processing under budgetary constraints. In ICDE, pages 928--939, 2009. Google ScholarDigital Library
- Prabhakant Sinha and Andris A. Zoltners. The multiple-choice knapsack problem. Operations Research, 27(3):503--515, 1979.Google ScholarDigital Library
- Lidan Wang, Donald Metzler, and Jimmy Lin. Ranking under temporal constraints. In CIKM, pages 79--88, 2010. Google ScholarDigital Library
- Justin Zobel and Alistair Moffat. Inverted files for text search engines. ACM Comput. Surv., 38(2), 2006. Google ScholarDigital Library
Index Terms
- Learning to rank under tight budget constraints
Recommendations
Learning to rank code examples for code search engines
Source code examples are used by developers to implement unfamiliar tasks by learning from existing solutions. To better support developers in finding existing solutions, code search engines are designed to locate and rank code examples relevant to user'...
Speeding up Document Ranking with Rank-based Features
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information RetrievalLearning to Rank (LtR) is an effective machine learning methodology for inducing high-quality document ranking functions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vectors, a machine-learned ...
On Application of Learning to Rank for E-Commerce Search
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalE-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines, and is thus also a key technology for E-Com search. While the use of ...
Comments