The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Expected reciprocal rank for graded relevance
Abstract
While numerous metrics for information retrieval are available in the case of binary relevance, there is only one commonly used metric for graded relevance, namely the Discounted Cumulative Gain (DCG). A drawback of DCG is its additive nature and the underlying independence assumption: a document in a given position has always the same gain and discount independently of the documents shown above it. Inspired by the "cascade" user model, we present a new editorial metric for graded relevance which overcomes this difficulty and implicitly discounts documents which are shown below very relevant documents. More precisely, this new metric is defined as the expected reciprocal length of time that the user will take to find a relevant document. This can be seen as an extension of the classical reciprocal rank to the graded relevance case and we call this metric Expected Reciprocal Rank (ERR). We conduct an extensive evaluation on the query logs of a commercial search engine and show that ERR correlates better with clicks metrics than other editorial metrics.
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Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836
Predicting clicks: estimating the click-through rate for new ads
Richardson, M., Dominowska, E., & Ragno, R. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242643 · Cited by 1,049
Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S. (2009) · Proceedings of the Second ACM International Conference on Web Search and Data Mining · doi:10.1145/1498759.1498766 · Cited by 1,020
Novelty and diversity in information retrieval evaluation
Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., & MacKinnon, I. (2008) · Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1390334.1390446 · Cited by 962
An experimental comparison of click position-bias models
Craswell, N., Zoeter, O., Taylor, M., & Ramsey, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341545 · Cited by 942
Rank-biased precision for measurement of retrieval effectiveness
Moffat, A., & Zobel, J. (2008) · ACM Transactions on Information Systems · doi:10.1145/1416950.1416952 · Cited by 620
Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval
Zhai, C., Cohen, W. W., & Lafferty, J. (2015) · ACM SIGIR Forum · doi:10.1145/2795403.2795405 · Cited by 558
A dynamic bayesian network click model for web search ranking
Chapelle, O., & Zhang, Y. (2009) · Proceedings of the 18th international conference on World wide web · doi:10.1145/1526709.1526711 · Cited by 537
Self-citationThe Philosophy of Information Retrieval Evaluation
Voorhees, E. M. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45691-0_34 · Cited by 389
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Vargas, S., & Castells, P. (2011) · Proceedings of the fifth ACM conference on Recommender systems · doi:10.1145/2043932.2043955 · Cited by 709
Test Collection Based Evaluation of Information Retrieval Systems
Sanderson, M. (2010) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000009 · Cited by 485
Chuklin, A., Markov, I., & de Rijke, M. (2015) · Synthesis Lectures on Information Concepts, Retrieval, and Services · doi:10.1007/978-3-031-02294-4 · Cited by 398
A Theoretical Analysis of NDCG Type Ranking Measures
Wang, Y., Wang, L., Li, Y., He, D., Liu, T.-Y., & Chen, W. (2013) · arXiv · doi:10.48550/arxiv.1304.6480 · Cited by 351
User Simulation for Evaluating Information Access Systems
Balog, K., & Zhai, C. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000098 · Cited by 52
Metadata
- Authors
- Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P.
- Venue
- Proceedings of the 18th ACM conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2009-11-02
- Pages
- 621-630
- OpenAlex
- W2113640060
- Cited by
- 832
- References
- 26
- FWCI
- 29.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009). Expected reciprocal rank for graded relevance. Proceedings of the 18th ACM conference on Information and knowledge management. https://doi.org/10.1145/1645953.1646033
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