The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
Abstract
This article examines the reliability of implicit feedback generated from clickthrough data and query reformulations in World Wide Web (WWW) search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average. We find that such relative preferences are accurate not only between results from an individual query, but across multiple sets of results within chains of query reformulations.
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Cites
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Optimizing search engines using clickthrough data
Joachims, T. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775067 · Cited by 3,873
Self-citationLearning to rank using gradient descent
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Broder, A. (2002) · ACM SIGIR Forum · doi:10.1145/792550.792552 · Cited by 1,944
Accurately interpreting clickthrough data as implicit feedback
Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005) · Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1076034.1076063 · Cited by 1,408
Self-citationReal life, real users, and real needs: a study and analysis of user queries on the web
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Needham, D., & Lloyd, C. (1968) · Economica · doi:10.2307/2552169 · Cited by 1,278
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2017)
Amendola, G., Ricca, F., & Truszczyński, M. (2017) · International Joint Conference on Artificial Intelligence · Cited by 1,179
Analysis of a very large web search engine query log
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Cited by
A Survey of Automatic Query Expansion in Information Retrieval
Carpineto, C., & Romano, G. (2012) · ACM Computing Surveys · doi:10.1145/2071389.2071390 · Cited by 1,109
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Unbiased Learning-to-Rank with Biased Feedback
Joachims, T., Swaminathan, A., & Schnabel, T. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018699 · Cited by 531
Self-citationAn Introduction to Neural Information Retrieval
Mitra, B., & Craswell, N. (2018) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000061 · Cited by 317
Understanding PubMed(R) user search behavior through log analysis
Islamaj Dogan, R., Murray, G. C., Neveol, A., & Lu, Z. (2009) · Database · doi:10.1093/database/bap018 · Cited by 265
Metadata
- Authors
- Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., & Gay, G.
- Venue
- ACM Transactions on Information Systems
- Published
- 2007-04
- Volume
- 25
- Issue
- 2
- Pages
- 7
- ISSN
- 1046-8188 (ISSN-L)
- 1558-2868
- OpenAlex
- W1974360117
- Cited by
- 677
- References
- 48
- FWCI
- 102.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., & Gay, G. (2007). Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search. ACM Transactions on Information Systems. https://doi.org/10.1145/1229179.1229181
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