research-article

Boosting Search Performance Using Query Variations

Authors:

Joel Mackenzie,

Alistair Moffat,

J. Shane CulpepperAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 37, Issue 4

Article No.: 41, Pages 1 - 25

https://doi.org/10.1145/3345001

Published: 04 October 2019 Publication History

Abstract

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. Query variations covering the same information need represent one way in which different sources of information might arise. However, when implemented in the obvious manner, fusion over query variations is not cost-effective, at odds with the usual web-search requirement for strict per-query efficiency guarantees. In this work, we propose a novel solution to query fusion by splitting the computation into two parts: one phase that is carried out offline, to generate pre-computed centroid answers for queries addressing broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. To achieve this, we make use of score-based fusion algorithms whose costs can be amortized via the pre-processing step and that can then be efficiently combined during subsequent per-query re-ranking operations. Experimental results using the ClueWeb12B collection and the UQV100 query variations demonstrate that centroid-based approaches allow improved retrieval effectiveness at little or no loss in query throughput or latency and within reasonable pre-processing requirements. We additionally show that queries that do not match any of the pre-computed clusters can be accurately identified and efficiently processed in our proposed ranking pipeline.

References

[1]

R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. 2009. Diversifying search results. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’09). 5--14.

[2]

G. Amati and C. J. van Rijsbergen. 2002. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20, 4 (2002), 357--389.

Digital Library

[3]

Y. Anava, A. Shtok, O. Kurland, and E. Rabinovich. 2016. A probabilistic fusion framework. In Proceedings of the International Conference on Theory of Information Retrieval (ICTIR’16). 1463--1472.

[4]

R. Baeza-Yates, A. Gionis, F. Junqueira, V. Murdock, V. Plachouras, and F. Silvestri. 2008. Design trade-offs for search engine caching. ACM Trans. Web 2, 4 (2008), 1--28.

Digital Library

[5]

P. Bailey, A. Moffat, F. Scholer, and P. Thomas. 2016. UQV100: A test collection with query variability. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’16). 725--728.

[6]

P. Bailey, A. Moffat, F. Scholer, and P. Thomas. 2017. Retrieval consistency in the presence of query variations. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’17). 395--404.

[7]

M. Barbaro and T. Zeller. 2006. A face is exposed for AOL searcher No. 4417749. Retrieved November 8, 2018 from https://nytimes.com/2006/08/09/technology/09aol.html.

[8]

N. J. Belkin, C. Cool, W. B. Croft, and J. P. Callan. 1993. The effect of multiple query variations on information retrieval system performance. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’93). 339--346.

[9]

M. Bendersky, D. Metzler, and W. B. Croft. 2012. Effective query formulation with multiple information sources. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’12). 443--452.

[10]

R. Benham and J. S. Culpepper. 2017. Risk-reward trade-offs in rank fusion. In Proceedings of the Australasian Document Computing Symposium (ADCS’17). 1:1--1:8.

[11]

R. Benham, J. S. Culpepper, L. Gallagher, X. Lu, and J. Mackenzie. 2018. Towards efficient and effective query variant generation. In Proceedings of the Conference on Design of Experimental Search 8 Information Retrieval Systems (DESIRES’18). 62--67.

[12]

R. Benham, L. Gallagher, J. Mackenzie, T. T. Damessie, R.-C. Chen, F. Scholer, A. Moffat, and J. S. Culpepper. 2017. RMIT at the 2017 TREC CORE track. In Proceedings of the Text Retrieval Conference (TREC’17).

[13]

R. Benham, L. Gallagher, J. Mackenzie, B. Liu, X. Lu, F. Scholer, A. Moffat, and J. S. Culpepper. 2018. RMIT at the 2018 TREC CORE track. In Proceedings of the Text Retrieval Conference (TREC’18).

[14]

B. Billerbeck, F. Scholer, H. E. Williams, and J. Zobel. 2003. Query expansion using associated queries. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’03). 2--9.

[15]

B. Billerbeck and J. Zobel. 2004. Techniques for efficient query expansion. In Proceedings of the Symposium on String Processing and Information Retrieval (SPIRE’04). 30--42.

[16]

D. Broccolo, C. Macdonald, S. Orlando, I. Ounis, R. Perego, F. Silvestri, and N. Tonellotto. 2013. Load-sensitive selective pruning for distributed search. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’13). 379--388.

[17]

A. Z. Broder, D. Carmel, M. Herscovici, A. Soffer, and J. Y. Zien. 2003. Efficient query evaluation using a two-level retrieval process. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’03). 426--434.

[18]

C. Buckley, G. Salton, J. Allan, and A. Singhal. 1995. Automatic query expansion using SMART: TREC 3. In Proceedings of the Text Retrieval Conference (TREC’95).

[19]

C. Burges, R. Ragno, and Q. V. Le. 2006. Learning to rank with nonsmooth cost functions. In Proceedings of the Conference on Neural Information Processing Systems (NIPS’06). 193--200.

[20]

C. Cadwalladr and E. Graham-Harrison. 2018. Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. Retrieved November 8. 2018 from https://theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-election. Accessed: 2018-11-08.

[21]

B. B. Cambazoglu, F. P. Junqueira, V. Plachouras, S. Banachowski, B. Cui, S. Lim, and B. Bridge. 2010. A refreshing perspective of search engine caching. In Proceedings of the International Conference on the World Wide Web (WWW’10). 181--190.

[22]

M.-A. Cartright, J. Allan, V. Lavrenko, and A. McGregor. 2010. Fast query expansion using approximations of relevance models. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’10). 1573--1576.

[23]

K. Chakrabarti, S. Chaudhuri, and V. Ganti. 2011. Interval-based pruning for top-k processing over compressed lists. In Proceedings of the International Conference on Data Engineering (ICDE’11). 709--720.

[24]

R.-C. Chen, L. Gallagher, R. Blanco, and J. S. Culpepper. 2017. Efficient cost-aware cascade ranking in multi-stage retrieval. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’17). 445--454.

[25]

G. V. Cormack, C. L. A. Clarke, and S. Büttcher. 2009. Reciprocal rank fusion outperforms Condorcet and individual rank learning methods. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’09). 758--759.

[26]

M. Crane, J. S. Culpepper, J. Lin, J. Mackenzie, and A. Trotman. 2017. A comparison of document-at-a-time and score-at-a-time query evaluation. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’17). 201--210.

[27]

N. Craswell and M. Szummer. 2007. Random walks on the click graph. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’07). 239--246.

[28]

V. Dang and W. B. Croft. 2010. Query reformulation using anchor text. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’10). 41--50.

[29]

J. Dean and L. A. Barroso. 2013. The tail at scale. Comm. ACM 56, 2 (2013), 74--80.

Digital Library

[30]

L. Dhulipala, I. Kabiljo, B. Karrer, G. Ottaviano, S. Pupyrev, and A. Shalita. 2016. Compressing graphs and indexes with recursive graph bisection. In Proceedings of the Conference on Knowledge Discovery and Data Mining (KDD’16). 1535--1544.

[31]

C. Dimopoulos, S. Nepomnyachiy, and T. Suel. 2013. Optimizing top-k document retrieval strategies for block-max indexes. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’13). 113--122.

[32]

S. Ding and T. Suel. 2011. Faster top-k document retrieval using block-max indexes. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’11). 993--1002.

[33]

T. Fagni, R. Perego, F. Silvestri, and S. Orlando. 2006. Boosting the performance of web search engines: Caching and prefetching query results by exploiting historical usage data. ACM Trans. Inf Syst. 24, 1 (2006), 51--78.

Digital Library

[34]

M. Fontoura, V. Josifovski, J. Liu, S. Venkatesan, X. Zhu, and J. Zien. 2011. Evaluation strategies for top-k queries over memory-resident inverted indexes. Proceedings of the Very Large Databases (VLDB) 4, 12 (2011), 1213--1224.

[35]

E. A. Fox and J. A. Shaw. 1993. Combination of multiple searches. In Proceedings of the Text Retrieval Conference (TREC’93). 243--252.

[36]

N. Fuhr. 2018. Some common mistakes in IR evaluation, and how they can be avoided. SIGIR Forum 51, 3 (2018), 32--41.

Digital Library

[37]

L. Gallagher, R.-C. Chen, R. Blanco, and J. S. Culpepper. 2019. Joint optimization of cascade ranking models. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’19). 15--23.

[38]

Q. Gan and T. Suel. 2009. Improved techniques for result caching in web search engines. In Proceedings of the International Conference on the World Wide Web (WWW’09). 431--440.

[39]

F. Hafizoglu, E. C. Kucukoglu, and I. S. Altingovde. 2017. On the efficiency of selective search. In Proceedings of the European Conference on Information Retrieval (ECIR’17). 705--712.

[40]

D. K. Harman. 1995. Overview of the third text retrieval conference (TREC-3). In Proceedings of the Text Retrieval Conference (TREC’95).

[41]

Y. He, J. Tang, H. Ouyang, C. Kang, D. Yin, and Y. Chang. 2016. Learning to rewrite queries. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’16). 1443--1452.

[42]

S. Huo, M. Zhang, Y. Liu, and S. Ma. 2014. Improving tail query performance by fusion model. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’14). 559--658.

[43]

K. Järvelin and J. Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20, 4 (2002), 422--446.

Digital Library

[44]

S. Kim, Y. He, S.-W. Hwang, S. Elnikety, and S. Choi. 2015. Delayed-dynamic-selective (DDS) prediction for reducing extreme tail latency in web search. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’15). 7--16.

[45]

Y. Kim, J. Callan, J. S. Culpepper, and A. Moffat. 2016. Does selective search benefit from WAND optimization? In Proceedings of the European Conference on Information Retrieval (ECIR’16). 145--158.

[46]

Y. Kim, J. Callan, J. S. Culpepper, and A. Moffat. 2017. Efficient distributed selective search. Inf. Retriev. 20, 3 (2017), 221--252.

Digital Library

[47]

J. Kong, A. Scott, and G. M. Goerg. 2016. Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering. Google Inc (2016). Retrieved from https://ai.google/research/pubs/pub45569.pdf.

[48]

A. K. Kozorovitsky and O. Kurland. 2011. Cluster-based fusion of retrieved lists. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’11). 893--902.

[49]

O. Kurland and J. S. Culpepper. 2018. Tutorial: Fusion in information retrieval. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’18). 1383--1386.

[50]

V. Lavrenko and J. Allan. 2006. Real-time query expansion in relevance models. IR 473, University of Massachusetts Amherst (2006).

[51]

V. Lavrenko and W. B. Croft. 2001. Relevance-based language models. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’01). 120--127.

[52]

C.-J. Lee, Q. Ai, W. B. Croft, and D. Sheldon. 2015. An optimization framework for merging multiple result lists. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’15). 303--312.

[53]

S. Liang, Z. Ren, and M. de Rijke. 2014. Fusion helps diversification. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’14). 303--312.

Digital Library

[54]

T.-Y. Liu. 2009. Learning to rank for information retrieval. Found. Trends Inf. Retriev. 3, 3 (2009), 225--331.

Digital Library

[55]

X. Lu, A. Moffat, and J. S. Culpepper. 2016. The effect of pooling and evaluation depth on IR metrics. Inf. Retriev. 19, 4 (2016), 416--445.

Digital Library

[56]

X. Lu, A. Moffat, and J. S. Culpepper. 2016. Efficient and effective higher order proximity modeling. In Proceedings of the International Conference on Theory of Information Retrieval (ICTIR’16). 21--30.

[57]

H. Ma and B. Wang. 2012. User-aware caching and prefetching query results in web search engines. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’12). 1163--1164.

[58]

C. Macdonald, R. L. T. Santos, and I. Ounis. 2013. The whens and hows of learning to rank for web search. Inf. Retriev. 16, 5 (2013), 584--628.

Digital Library

[59]

C. Macdonald, R. L. T. Santos, I. Ounis, and B. He. 2013. About learning models with multiple query-dependent features. ACM Trans. Inf. Syst. 31, 3 (2013), 11:1--11:39.

Digital Library

[60]

J. Mackenzie. 2017. Managing tail latencies in large scale IR systems. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’17). 1369.

Digital Library

[61]

J. Mackenzie, J. S. Culpepper, R. Blanco, M. Crane, and J. Lin. 2018. Query driven algorithm selection in early stage retrieval. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’18). 396--404.

[62]

J. Mackenzie, A. Mallia, M. Petri, J. S. Culpepper, and T. Suel. 2019. Compressing inverted indexes with recursive graph bisection: A reproducibility study. In Proceedings of the European Conference on Information Retrieval (ECIR’19). 339--352.

[63]

A. Mallia, G. Ottaviano, E. Porciani, N. Tonellotto, and R. Venturini. 2017. Faster blockmax WAND with variable-sized blocks. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’17). 625--634.

[64]

A. Mallia, M. Siedlaczek, and T. Suel. 2019. An experimental study of index compression and DAAT query processing methods. In Proceedings of the European Conference on Information Retrieval (ECIR’19). 353--368.

[65]

D. Metzler and W. B. Croft. 2005. A Markov random field model for term dependencies. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’05). 472--479.

[66]

D. Metzler, S. Dumais, and C. Meek. 2007. Similarity measures for short segments of text. In Proceedings of the European Conference on Information Retrieval (ECIR’07). 16--27.

[67]

A. Moffat. 2016. Judgment pool effects caused by query variations. In Proceedings of the Australasian Document Computing Symposium (ADCS’16). 65--68.

Digital Library

[68]

A. Moffat, P. Bailey, F. Scholer, and P. Thomas. 2017. Incorporating user expectations and behavior into the measurement of search effectiveness. ACM Trans. Inf. Syst. 35, 3 (2017), 24:1--24:38.

Digital Library

[69]

A. Moffat, F. Scholer, P. Thomas, and P. Bailey. 2015. Pooled evaluation over query variations: Users are as diverse as systems. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’15). 1759--1762.

[70]

A. Moffat and J. Zobel. 2008. Rank-biased precision for measurement of retrieval effectiveness. ACM Trans. Inf. Syst. 27, 1 (2008), 2.1--2.27.

Digital Library

[71]

A. Mourão and J. Magalhães. 2018. Low-complexity supervised rank fusion models. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’18). 1691--1694.

[72]

G. Ottaviano and R. Venturini. 2014. Partitioned Elias-Fano indexes. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’14). 273--282.

[73]

J. Ponte and W. B. Croft. 1998. A language modeling approach to information retrieval. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’98). 275--281.

[74]

F. Radlinski, M. Kurup, and T. Joachims. 2008. How does clickthrough data reflect retrieval quality? In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’08). 43--52.

[75]

S. E. Robertson and S. Walker. 2000. Microsoft Cambridge at TREC-9: Filtering track. In Proceedings of the Text Retrieval Conference (TREC’00).

[76]

J. J. Rocchio. 1971. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing. 313--323.

[77]

F. Scholer and H. E. Williams. 2002. Query association for effective retrieval. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’02). 324--331.

[78]

D. Sheldon, M. Shokouhi, M. Szummer, and N. Craswell. 2011. LambdaMerge: Merging the results of query reformulations. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’11). 795--804.

[79]

J. Shen, M. Karimzadehgan, M. Bendersky, Z. Qin, and D. Metzler. 2018. Multi-task learning for email search ranking with auxiliary query clustering. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’18). 2127--2135.

[80]

T. Strohman, H. Turtle, and W. B. Croft. 2005. Optimization strategies for complex queries. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’05). 219--225.

[81]

H. Turtle and W. B. Croft. 1991. Evaluation of an inference network-based retrieval model. 9, 3 (1991), 187--222.

[82]

H. R. Turtle and J. Flood. 1995. Query evaluation: Strategies and optimizations. Inf. Process. Manage. 31, 6 (1995), 831--850.

Digital Library

[83]

C. C. Vogt. 1999. Adaptive Combination of Evidence for Information Retrieval. Ph.D. Dissertation. Retrieved from http://cseweb.ucsd.edu/groups/guru/docs/theses/vogt-thesis.pdf.

[84]

C. C. Vogt. 2000. How much more is better? Characterizing the effects of adding more IR systems to a combination. In Proceedings of the Recherche d’Information etses Applications (RIAO’00). 457--475.

[85]

C. C. Vogt and G. W. Cottrell. 1999. Fusion via a linear combination of scores. Inf. Retriev. 1, 3 (1999), 151--173.

Digital Library

[86]

J. Wang, E. Lo, M. L. Yiu, J. Tong, G. Wang, and X. Liu. 2014. Cache design of SSD-based search engine architectures: An experimental study. ACM Trans. Inf. Syst. 32, 4 (2014), 1--26.

Digital Library

[87]

J. R. Wen, J. Y. Nie, and H. J. Zhang. 2001. Clustering user queries of a search engine. In Proceedings of the International Conference on the World Wide Web (WWW’01). 162--168.

[88]

J. R. Wen, J. Y. Nie, and H. J. Zhang. 2002. Query clustering using user logs. ACM Trans. Inf. Syst. 20, 1 (2002), 59--81.

Digital Library

[89]

H. Wu and H. Fang. 2013. An incremental approach to efficient pseudo-relevance feedback. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’13). 553--562.

[90]

G.-R. Xue, H.-J. Zeng, Z. Chen, Y. Yu, W.-Y. Ma, W. Xi, and W. Fan. 2004. Optimizing web search using web click-through data. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’04). 118--126.

[91]

X. Xue and W. B. Croft. 2013. Modeling reformulation using query distributions. ACM Trans. Inf. Syst. 31, 2 (2013), 6:1--6:34.

Digital Library

[92]

J.-M. Yun, Y. He, S. Elnikety, and S. Ren. 2015. Optimal aggregation policy for reducing tail latency of web search. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR’15). 63--72.

[93]

J. Zobel and A. Moffat. 2006. Inverted files for text search engines. Comput. Surv. 38, 2 (2006), 6.1--6.56.

Digital Library

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Index Terms

Boosting Search Performance Using Query Variations
1. Information systems
  1. Information retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 37, Issue 4

October 2019

299 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3357218

Editor:
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Publication History

Published: 04 October 2019

Accepted: 01 July 2019

Revised: 01 May 2019

Received: 01 November 2018

Published in TOIS Volume 37, Issue 4

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https://dl.acm.org/doi/10.1007/s10791-022-09407-w
Breuer TFuhr NSchaer P(2022)Validating Simulations of User Query VariantsAdvances in Information Retrieval10.1007/978-3-030-99736-6_6(80-94)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99736-6_6
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