Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
  • Jahani H, Azzopardi L and Sanderson M. (2024). Measuring the retrievability of digital library content using analytics data. Journal of the Association for Information Science and Technology. 10.1002/asi.24886.

    https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24886

  • Rony M, Sahoo S, Khan A, Friedl K, Sudhi V and Süß C. (2024). Incorporating Query Recommendation for Improving In-Car Conversational Search. Advances in Information Retrieval. 10.1007/978-3-031-56069-9_36. (304-312).

    https://link.springer.com/10.1007/978-3-031-56069-9_36

  • Zhu D, Fan S, Zeng X, Xi R and Hou M. (2023). Graph-Attention-Network-Based Cost Estimation Model in Materialized View Environment 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS). 10.1109/ICPADS60453.2023.00198. 979-8-3503-3071-7. (1388-1396).

    https://ieeexplore.ieee.org/document/10476241/

  • Yu P, Rahimi R, Huang Z and Allan J. Search Result Diversification Using Query Aspects as Bottlenecks. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (3040-3051).

    https://doi.org/10.1145/3583780.3615050

  • Keyvan K and Huang J. (2022). How to Approach Ambiguous Queries in Conversational Search: A Survey of Techniques, Approaches, Tools, and Challenges. ACM Computing Surveys. 55:6. (1-40). Online publication date: 30-Jun-2023.

    https://doi.org/10.1145/3534965

  • Wang Z, Tu Y, Rosset C, Craswell N, Wu M and Ai Q. Zero-shot Clarifying Question Generation for Conversational Search. Proceedings of the ACM Web Conference 2023. (3288-3298).

    https://doi.org/10.1145/3543507.3583420

  • Deckers N, Fröbe M, Kiesel J, Pandolfo G, Schröder C, Stein B and Potthast M. The Infinite Index: Information Retrieval on Generative Text-To-Image Models. Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. (172-186).

    https://doi.org/10.1145/3576840.3578327

  • Yu P, Rahimi R and Allan J. Towards Explainable Search Results. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. (669-680).

    https://doi.org/10.1145/3477495.3532067

  • Liu J, Zhang H, Xia X, Lo D, Zou Y, Hassan A and Li S. (2021). An exploratory study on the repeatedly shared external links on Stack Overflow. Empirical Software Engineering. 10.1007/s10664-021-10028-y. 27:1. Online publication date: 1-Jan-2022.

    https://link.springer.com/10.1007/s10664-021-10028-y

  • Fröbe M, Günther S, Probst M, Potthast M and Hagen M. (2022). The Power of Anchor Text in the Neural Retrieval Era. Advances in Information Retrieval. 10.1007/978-3-030-99736-6_38. (567-583).

    https://link.springer.com/10.1007/978-3-030-99736-6_38

  • Ma Z, Dou Z, Xu W, Zhang X, Jiang H, Cao Z and Wen J. Pre-training for Ad-hoc Retrieval. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. (1212-1221).

    https://doi.org/10.1145/3459637.3482286

  • Liu B, Lu X and Culpepper J. (2021). Strong natural language query generation. Information Retrieval Journal. 10.1007/s10791-021-09395-3.

    https://link.springer.com/10.1007/s10791-021-09395-3

  • Sheetrit E, Fyodorov Y, Raiber F and Kurland O. Recommending Search Queries in Documents Using Inter N-Gram Similarities. Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. (211-220).

    https://doi.org/10.1145/3471158.3472252

  • Kim J, Song Y and Hwang S. Web Document Encoding for Structure-Aware Keyphrase Extraction. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. (1823-1827).

    https://doi.org/10.1145/3404835.3463067

  • Chen J, Mao J, Liu Y, Zhang F, Zhang M and Ma S. Towards a Better Understanding of Query Reformulation Behavior in Web Search. Proceedings of the Web Conference 2021. (743-755).

    https://doi.org/10.1145/3442381.3450127

  • Englmeier K. (2021). User-Centered Detection of Fake News and Misinformation - Design and Prototypical Implementation in the System Contexter. Human Systems Engineering and Design III. 10.1007/978-3-030-58282-1_1. (3-8).

    https://link.springer.com/10.1007/978-3-030-58282-1_1

  • Shajalal M and Aono M. (2021). Health Information Retrieval. Signal Processing Techniques for Computational Health Informatics. 10.1007/978-3-030-54932-9_8. (193-207).

    http://link.springer.com/10.1007/978-3-030-54932-9_8

  • Hawking D, Billerbeck B, Thomas P and Craswell N. (2020). Simulating Information Retrieval Test Collections. Synthesis Lectures on Information Concepts, Retrieval, and Services. 10.2200/S01043ED1V01Y202008ICR071. 12:2. (1-184). Online publication date: 1-Sep-2020.

    https://www.morganclaypool.com/doi/10.2200/S01043ED1V01Y202008ICR071

  • Zhang K, Xiong C, Liu Z and Liu Z. Selective Weak Supervision for Neural Information Retrieval. Proceedings of The Web Conference 2020. (474-485).

    https://doi.org/10.1145/3366423.3380131

  • ARSLAN A. (2020). ON THE USEFULNESS OF HTML META ELEMENTS FOR WEB RETRIEVAL. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. 10.18038/estubtda.615103. 21:1. (182-198).

    http://dergipark.org.tr/en/doi/10.18038/estubtda.615103

  • Englmeier K. (2020). Named Entities and Their Role in Creating Context Information. Procedia Computer Science. 10.1016/j.procs.2020.09.243. 176. (2069-2076).

    https://linkinghub.elsevier.com/retrieve/pii/S1877050920321463

  • Liu H, Yin D and Tang J. (2020). Query Rewriting. Query Understanding for Search Engines. 10.1007/978-3-030-58334-7_6. (129-144).

    http://link.springer.com/10.1007/978-3-030-58334-7_6

  • Zolitschka J. (2020). A Novel Multi-agent-based Chatbot Approach to Orchestrate Conversational Assistants. Business Information Systems. 10.1007/978-3-030-53337-3_8. (103-117).

    https://link.springer.com/10.1007/978-3-030-53337-3_8

  • Benham R, Mackenzie J, Moffat A and Culpepper J. (2019). Boosting Search Performance Using Query Variations. ACM Transactions on Information Systems. 37:4. (1-25). Online publication date: 31-Oct-2019.

    https://doi.org/10.1145/3345001

  • Kuzi S, Narwekar A, Pampari A and Zhai C. Help Me Search. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. (1221-1224).

    https://doi.org/10.1145/3331184.3331362

  • Fails J, Pera M, Anuyah O, Kennington C, Wright K and Bigirimana W. Query Formulation Assistance for Kids. Proceedings of the 18th ACM International Conference on Interaction Design and Children. (109-120).

    https://doi.org/10.1145/3311927.3323131

  • Onan A. (2019). Semantic Query Suggestion Based on Optimized Random Forests. Artificial Intelligence and Algorithms in Intelligent Systems. 10.1007/978-3-319-91189-2_10. (91-102).

    http://link.springer.com/10.1007/978-3-319-91189-2_10

  • Chen J, Mao J, Liu Y, Zhang M and Ma S. (2019). Investigating Query Reformulation Behavior of Search Users. Information Retrieval. 10.1007/978-3-030-31624-2_4. (39-51).

    http://link.springer.com/10.1007/978-3-030-31624-2_4

  • Jiang J and Wang W. RIN. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. (197-206).

    https://doi.org/10.1145/3269206.3271808

  • Raza M, Mokhtar R and Ahmad N. (2018). A survey of statistical approaches for query expansion. Knowledge and Information Systems. 10.1007/s10115-018-1269-8.

    http://link.springer.com/10.1007/s10115-018-1269-8

  • Keikha A, Ensan F and Bagheri E. (2018). Query expansion using pseudo relevance feedback on wikipedia. Journal of Intelligent Information Systems. 50:3. (455-478). Online publication date: 1-Jun-2018.

    https://doi.org/10.1007/s10844-017-0466-3

  • Onal K, Zhang Y, Altingovde I, Rahman M, Karagoz P, Braylan A, Dang B, Chang H, Kim H, Mcnamara Q, Angert A, Banner E, Khetan V, Mcdonnell T, Nguyen A, Xu D, Wallace B, Rijke M and Lease M. (2018). Neural information retrieval. Information Retrieval. 21:2-3. (111-182). Online publication date: 1-Jun-2018.

    https://doi.org/10.1007/s10791-017-9321-y

  • Alonso O, Kandylas V and Tremblay S. How it Happened. Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries. (193-202).

    https://doi.org/10.1145/3197026.3197034

  • (2018). Cross-lingual analysis of English and Chinese web search. International Journal of Web and Grid Services. 14:4. (376-399). Online publication date: 1-Jan-2018.

    /doi/10.5555/3292946.3292949

  • Han B, Chen L and Tian X. (2018). Knowledge based collection selection for distributed information retrieval. Information Processing and Management: an International Journal. 54:1. (116-128). Online publication date: 1-Jan-2018.

    https://doi.org/10.1016/j.ipm.2017.10.002

  • Brusilovsky P, Smyth B and Shapira B. (2018). Social Search. Social Information Access. 10.1007/978-3-319-90092-6_7. (213-276).

    http://link.springer.com/10.1007/978-3-319-90092-6_7

  • Li X, Liu Y, Li X, Luo C, Nie J, Zhang M and Ma S. (2018). Hierarchical Attention Network for Context-Aware Query Suggestion. Information Retrieval Technology. 10.1007/978-3-030-03520-4_17. (173-186).

    https://link.springer.com/10.1007/978-3-030-03520-4_17

  • Cai R, Liu Y, Zhang M and Ma S. (2018). Translating Embeddings for Modeling Query Reformulation. Information Retrieval. 10.1007/978-3-030-01012-6_1. (3-15).

    http://link.springer.com/10.1007/978-3-030-01012-6_1

  • Bashir S. (2017). Broken link repairing system for constructing contextual information portals. Journal of King Saud University - Computer and Information Sciences. 10.1016/j.jksuci.2017.12.013. Online publication date: 1-Dec-2017.

    https://linkinghub.elsevier.com/retrieve/pii/S1319157817302380

  • Liu M, Fang Y, Choulos A, Park D and Hu X. (2017). Product review summarization through question retrieval and diversification. Information Retrieval Journal. 10.1007/s10791-017-9311-0. 20:6. (575-605). Online publication date: 1-Dec-2017.

    http://link.springer.com/10.1007/s10791-017-9311-0

  • Guisado-Gámez J, Prat-Pérez A and Larriba-Pey J. Structural Query Expansion via motifs from Wikipedia. Proceedings of the ExploreDB'17. (1-6).

    https://doi.org/10.1145/3077331.3077342

  • Jena A and Balabantaray R. (2017). Semantic desktop search application for Hindi-English code-mixed user query with query sequence analysis 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). 10.1109/RTEICT.2017.8256734. 978-1-5090-3704-9. (930-934).

    http://ieeexplore.ieee.org/document/8256734/

  • Mivule K. (2017). Web Search Query Privacy, an End-User Perspective. Journal of Information Security. 10.4236/jis.2017.81005. 08:01. (56-74).

    http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jis.2017.81005

  • Mohd Yunus M, Mustapha A, Samsudin N, Nik Hisyamudin M, Amir K, Al Emran I, Mohd Rasidi I, Mohd Faizal M, Mohd Azlis Sani M, Ahmad Mubarak T, Izzuddin Z and Sofian M. (2017). Analysis of translated query in Quranic Malay and English translation documents with stemmer. MATEC Web of Conferences. 10.1051/matecconf/201713500069. 135. (00069).

    http://www.matec-conferences.org/10.1051/matecconf/201713500069

  • Song R, Wang D, Nie J, Wen J and Yu Y. (2016). Enhancing web search with queries of equivalent intents. Information Retrieval Journal. 10.1007/s10791-016-9288-0. 19:6. (573-593). Online publication date: 1-Dec-2016.

    http://link.springer.com/10.1007/s10791-016-9288-0

  • Azizan A, Bakar Z and Noah S. (2016). Query reformulation using ontology and keyword for durian web search 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP). 10.1109/INFRKM.2016.7806342. 978-1-5090-2954-9. (94-100).

    http://ieeexplore.ieee.org/document/7806342/

  • Liu M, Fang Y, Park D, Hu X and Yu Z. Retrieving Non-Redundant Questions to Summarize a Product Review. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. (385-394).

    https://doi.org/10.1145/2911451.2911544

  • Wang Q, Dimopoulos C and Suel T. Fast First-Phase Candidate Generation for Cascading Rankers. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. (295-304).

    https://doi.org/10.1145/2911451.2911515

  • Yang G, Sloan M and Wang J. (2016). Dynamic Information Retrieval Modeling. Synthesis Lectures on Information Concepts, Retrieval, and Services. 10.2200/S00718ED1V01Y201605ICR049. 8:3. (1-144). Online publication date: 15-Jun-2016.

    http://www.morganclaypool.com/doi/10.2200/S00718ED1V01Y201605ICR049

  • Jiang J and Ni C. What Affects Word Changes in Query Reformulation During a Task-based Search Session?. Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval. (111-120).

    https://doi.org/10.1145/2854946.2854978

  • Hagen M, Michel M and Stein B. (2016). Simulating Ideal and Average Users. Information Retrieval Technology. 10.1007/978-3-319-48051-0_11. (138-154).

    https://link.springer.com/10.1007/978-3-319-48051-0_11

  • Azizan A and Bakar Z. Seeking information in specific domain using domain name, crop characteristic and ontology. Proceedings of the 2015 11th International Conference on Innovations in Information Technology (IIT). (58-62).

    https://doi.org/10.1109/INNOVATIONS.2015.7381515

  • Jabeen F and Khusro S. (2015). Quality-protected folksonomy maintenance approaches: a brief survey. The Knowledge Engineering Review. 10.1017/S0269888915000120. 30:5. (521-544). Online publication date: 1-Nov-2015.

    https://www.cambridge.org/core/product/identifier/S0269888915000120/type/journal_article

  • Azizan A and Bakar Z. (2015). Query Reformulation Using Crop Characteristic in Specific Domain Search 2015 IEEE European Modelling Symposium (EMS). 10.1109/EMS.2015.88. 978-1-5090-0206-1. (374-379).

    http://ieeexplore.ieee.org/document/7579855/

  • Carterette B, Bah A and Zengin M. Dynamic Test Collections for Retrieval Evaluation. Proceedings of the 2015 International Conference on The Theory of Information Retrieval. (91-100).

    https://doi.org/10.1145/2808194.2809470

  • Mottin D, Bonchi F and Gullo F. Graph Query Reformulation with Diversity. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (825-834).

    https://doi.org/10.1145/2783258.2783343

  • Gupta M and Bendersky M. Information Retrieval with Verbose Queries. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. (1121-1124).

    https://doi.org/10.1145/2766462.2767877

  • Nguyen T, Kanhabua N, Nejdl W and Niederée C. Mining Relevant Time for Query Subtopics in Web Archives. Proceedings of the 24th International Conference on World Wide Web. (1357-1362).

    https://doi.org/10.1145/2740908.2741702

  • Santos R, Macdonald C and Ounis I. (2015). Search Result Diversification. Foundations and Trends in Information Retrieval. 9:1. (1-90). Online publication date: 1-Mar-2015.

    https://doi.org/10.1561/1500000040

  • Bing L, Lam W, Wong T and Jameel S. (2015). Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context. ACM Transactions on Information Systems. 33:2. (1-38). Online publication date: 26-Feb-2015.

    https://doi.org/10.1145/2699666

  • Azizan A, Bakar Z and Noah S. (2014). Analysis of retrieval result on ontology-based query reformulation 2014 International Conference on Computer, Communications, and Control Technology (I4CT). 10.1109/I4CT.2014.6914183. 978-1-4799-4555-9. (244-248).

    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6914183

  • Jiang J, Ke Y, Chien P and Cheng P. Learning user reformulation behavior for query auto-completion. Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. (445-454).

    https://doi.org/10.1145/2600428.2609614

  • Gutierrez-Soto C and Hubert G. (2014). Randomized algorithm for Information Retrieval using past search results 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS). 10.1109/RCIS.2014.6861068. 978-1-4799-2393-9. (1-9).

    http://ieeexplore.ieee.org/document/6861068/

  • Guisado-Gámez J, Dominguez-Sal D and Larriba-Pey J. Massive Query Expansion by Exploiting Graph Knowledge Bases for Image Retrieval. Proceedings of International Conference on Multimedia Retrieval. (33-40).

    https://doi.org/10.1145/2578726.2578737

  • Bah A and Carterette B. (2014). Aggregating Results from Multiple Related Queries to Improve Web Search over Sessions. Information Retrieval Technology. 10.1007/978-3-319-12844-3_15. (172-183).

    http://link.springer.com/10.1007/978-3-319-12844-3_15

  • Kato M, Sakai T and Tanaka K. (2013). When do people use query suggestion? A query suggestion log analysis. Information Retrieval. 10.1007/s10791-012-9216-x. 16:6. (725-746). Online publication date: 1-Dec-2013.

    http://link.springer.com/10.1007/s10791-012-9216-x

  • Dietz L, Wang Z, Huston S and Croft W. Retrieving opinions from discussion forums. Proceedings of the 22nd ACM international conference on Information & Knowledge Management. (1225-1228).

    https://doi.org/10.1145/2505515.2507861

  • Kim S and Lee J. Subtopic Mining Based on Head-Modifier Relation and Co-occurrence of Intents Using Web Documents. Proceedings of the 4th International Conference on Information Access Evaluation. Multilinguality, Multimodality, and Visualization - Volume 8138. (179-191).

    https://doi.org/10.1007/978-3-642-40802-1_22

  • Jiang J, Jeng W and He D. How do users respond to voice input errors?. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. (143-152).

    https://doi.org/10.1145/2484028.2484092

  • Xue X and Croft W. (2013). Modeling reformulation using query distributions. ACM Transactions on Information Systems. 31:2. (1-34). Online publication date: 1-May-2013.

    https://doi.org/10.1145/2457465.2457466

  • Lin Y, Lin H, Xu K and Sun X. (2013). Learning to rank using smoothing methods for language modeling. Journal of the American Society for Information Science and Technology. 10.1002/asi.22789. 64:4. (818-828). Online publication date: 1-Apr-2013.

    https://onlinelibrary.wiley.com/doi/10.1002/asi.22789

  • Craswell N, Billerbeck B, Fetterly D and Najork M. Robust query rewriting using anchor data. Proceedings of the sixth ACM international conference on Web search and data mining. (335-344).

    https://doi.org/10.1145/2433396.2433440

  • Gutiérrez-Soto C and Hubert G. (2013). Evaluating the Interest of Revamping Past Search Results. Database and Expert Systems Applications. 10.1007/978-3-642-40173-2_9. (73-80).

    http://link.springer.com/10.1007/978-3-642-40173-2_9

  • Jiang J, He D, Han S, Yue Z and Ni C. Contextual evaluation of query reformulations in a search session by user simulation. Proceedings of the 21st ACM international conference on Information and knowledge management. (2635-2638).

    https://doi.org/10.1145/2396761.2398710

  • Dang V, Kumaran G and Troy A. Domain dependent query reformulation for web search. Proceedings of the 21st ACM international conference on Information and knowledge management. (1045-1054).

    https://doi.org/10.1145/2396761.2398401

  • He J, Hollink V and de Vries A. Combining implicit and explicit topic representations for result diversification. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. (851-860).

    https://doi.org/10.1145/2348283.2348397

  • Zhao L and Callan J. Automatic term mismatch diagnosis for selective query expansion. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. (515-524).

    https://doi.org/10.1145/2348283.2348354

  • Dang V and Croft W. Diversity by proportionality. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. (65-74).

    https://doi.org/10.1145/2348283.2348296

  • Liu Y, Song R, Chen Y, Nie J and Wen J. Adaptive query suggestion for difficult queries. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. (15-24).

    https://doi.org/10.1145/2348283.2348289

  • Adeyanju I, Song D, Albakour M, Kruschwitz U, De Roeck A and Fasli M. Adaptation of the concept hierarchy model with search logs for query recommendation on intranets. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. (5-14).

    https://doi.org/10.1145/2348283.2348288

  • Adeyanju I, Song D, Albakour M, Kruschwitz U, De Roeck A and Fasli M. Learning from users' querying experience on intranets. Proceedings of the 21st International Conference on World Wide Web. (755-764).

    https://doi.org/10.1145/2187980.2188197

  • Song Y, Zhou D and He L. Query suggestion by constructing term-transition graphs. Proceedings of the fifth ACM international conference on Web search and data mining. (353-362).

    https://doi.org/10.1145/2124295.2124339

  • Zuo J, Wang M, Wan J, Wu G and Wu S. (2012). Modified Information Retrieval Model Based on Markov Network. Network Computing and Information Security. 10.1007/978-3-642-35211-9_40. (307-314).

    http://link.springer.com/10.1007/978-3-642-35211-9_40

  • Zuo J and Wang M. A Query Reformulation Model Using Markov Graphic Method. Proceedings of the 2011 International Conference on Asian Language Processing. (119-122).

    https://doi.org/10.1109/IALP.2011.62

  • Dang V, Xue X and Croft W. Inferring query aspects from reformulations using clustering. Proceedings of the 20th ACM international conference on Information and knowledge management. (2117-2120).

    https://doi.org/10.1145/2063576.2063904

  • Bing L, Lam W and Wong T. Using query log and social tagging to refine queries based on latent topics. Proceedings of the 20th ACM international conference on Information and knowledge management. (583-592).

    https://doi.org/10.1145/2063576.2063663

  • Ganguly D, Leveling J and Jones G. Simulation of within-session query variations using a text segmentation approach. Proceedings of the Second international conference on Multilingual and multimodal information access evaluation. (89-94).

    /doi/10.5555/2045274.2045288

  • Asadi N, Metzler D, Elsayed T and Lin J. Pseudo test collections for learning web search ranking functions. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. (1073-1082).

    https://doi.org/10.1145/2009916.2010058

  • Jain A, Ozertem U and Velipasaoglu E. Synthesizing high utility suggestions for rare web search queries. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. (805-814).

    https://doi.org/10.1145/2009916.2010024

  • Sheldon D, Shokouhi M, Szummer M and Craswell N. LambdaMerge. Proceedings of the fourth ACM international conference on Web search and data mining. (795-804).

    https://doi.org/10.1145/1935826.1935930

  • Xue X, Croft W and Smith D. Modeling reformulation using passage analysis. Proceedings of the 19th ACM international conference on Information and knowledge management. (1497-1500).

    https://doi.org/10.1145/1871437.1871656

  • Zhao L and Callan J. Term necessity prediction. Proceedings of the 19th ACM international conference on Information and knowledge management. (259-268).

    https://doi.org/10.1145/1871437.1871474

  • Huurnink B, Hofmann K, De Rijke M and Bron M. Validating query simulators. Proceedings of the 2010 international conference on Multilingual and multimodal information access evaluation: cross-language evaluation forum. (40-51).

    /doi/10.5555/1889174.1889183

  • Dang V, Bendersky M and Croft W. Learning to rank query reformulations. Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. (807-808).

    https://doi.org/10.1145/1835449.1835626

  • Ma Y, Lin H and Jin S. (2010). A Revised SimRank Approach for Query Expansion. Information Retrieval Technology. 10.1007/978-3-642-17187-1_53. (564-575).

    http://link.springer.com/10.1007/978-3-642-17187-1_53