Please wait a minute...

Frontiers of Computer Science

Front. Comput. Sci.    2021, Vol. 15 Issue (1) : 151601     https://doi.org/10.1007/s11704-020-9159-0
REVIEW ARTICLE
Information retrieval: a view from the Chinese IR community
Zhumin CHEN1, Xueqi CHENG2, Shoubin DONG3, Zhicheng DOU4, Jiafeng GUO2(), Xuanjing HUANG5, Yanyan LAN2(), Chenliang LI6, Ru LI7, Tie-Yan LIU8, Yiqun LIU9(), Jun MA1, Bing QIN10, Mingwen WANG11, Jirong WEN4, Jun XU4, Min ZHANG9, Peng ZHANG12, Qi ZHANG5
1. School of Computer Science and Technology, Shandong University, Jinan 250100, China
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
3. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
4. School of Information, Renmin University of China, Beijing 100872, China
5. School of Computer Science, Fudan University, Shanghai 200433, China
6. School of Cyber Science and Engineering,Wuhan University,Wuhan 430072, China
7. School of Big Data, Shanxi University, Taiyuan 200433, China
8. Microsoft Research Asia, Beijing 100080, China
9. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
10. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
11. School of Computer Information and Engineering, Jiangxi Normal University, Nanchang 330022, China
12. School of Computer Science and Technology, Tianjin University, Tianjin 300072, China
Download: PDF(502 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

During a two-day strategic workshop in February 2018, 22 information retrieval researchers met to discuss the future challenges and opportunities within the field. The outcome is a list of potential research directions, project ideas, and challenges. This report describes themajor conclusionswe have obtained during the workshop. A key result is that we need to open our mind to embrace a broader IR field by rethink the definition of information, retrieval, user, system, and evaluation of IR. By providing detailed discussions on these topics, this report is expected to inspire our IR researchers in both academia and industry, and help the future growth of the IR research community.

Keywords information retrieval      redefinition      information      scope of retrieval      retrieval models      users      system architecture      evaluation     
Corresponding Author(s): Jiafeng GUO,Yanyan LAN,Yiqun LIU   
Just Accepted Date: 27 December 2019   Issue Date: 24 September 2020
 Cite this article:   
Zhumin CHEN,Xueqi CHENG,Shoubin DONG, et al. Information retrieval: a view from the Chinese IR community[J]. Front. Comput. Sci., 2021, 15(1): 151601.
 URL:  
http://journal.hep.com.cn/fcs/EN/10.1007/s11704-020-9159-0
http://journal.hep.com.cn/fcs/EN/Y2021/V15/I1/151601
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Zhumin CHEN
Xueqi CHENG
Shoubin DONG
Zhicheng DOU
Jiafeng GUO
Xuanjing HUANG
Yanyan LAN
Chenliang LI
Ru LI
Tie-Yan LIU
Yiqun LIU
Jun MA
Bing QIN
Mingwen WANG
Jirong WEN
Jun XU
Min ZHANG
Peng ZHANG
Qi ZHANG
1 V Bush. As we may think. The Atlantic Monthly, 1945, 176(1): 101–108
2 C Clarke. From the chair... ACM SIGIR Forum, 2016, 50(1): 1
3 J Zobel, A Moffat. Inverted files for text search engines. ACM Computing Surveys (CSUR), 2006, 38(2): 6
https://doi.org/10.1145/1132956.1132959
4 G Salton, A Wong, C S Yang. A vector space model for automatic indexing. Communications of the ACM, 1975, 18(11): 613–620
https://doi.org/10.1145/361219.361220
5 S Robertson, H Zaragoza. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval, 2009, 3(4): 333–389
https://doi.org/10.1561/1500000019
6 Y Lv, C Zhai. Positional language models for information retrieval. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 299–306
https://doi.org/10.1145/1571941.1571994
7 C Zhai, J Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. ACM SIGIR Forum, 2017, 51(2): 268–276
https://doi.org/10.1145/3130348.3130377
8 L Page, S Brin, R Motwani, T Winograd. The pagerank citation ranking: bringing order to the web. Technical Report, Stanford InfoLab, 1999
9 J M Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 1999, 46(5): 604–632
https://doi.org/10.1145/324133.324140
10 C P Chen, C Y Zhang. Data-intensive applications, challenges, techniques and technologies: a survey on big data. Information Sciences, 2014, 275: 314–347
https://doi.org/10.1016/j.ins.2014.01.015
11 M Sanderson, W B Croft. The history of information retrieval research. Proceedings of the IEEE, 2012, 100 (Special Centennial Issue): 1444–1451
https://doi.org/10.1109/JPROC.2012.2189916
12 S Chaudhuri, U Dayal. An overview of data warehousing and olap technology. ACM Sigmod Record, 1997, 26(1): 65–74
https://doi.org/10.1145/248603.248616
13 P Borlund. The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Information Research, 2003, 8(3): 289–291
14 G Hinton, L Deng, D Yu, G Dahl, A R Mohamed, N Jaitly, A Senior, V Vanhoucke, P Nguyen, B Kingsbury. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 2012, 29(6): 82–97
https://doi.org/10.1109/MSP.2012.2205597
15 Y LeCun, Y Bengio. Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, 1995, 3361(10): 1995
16 R Socher, E H Huang, J Pennin, C D Manning, A Y Ng. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Proceedings of Advances in Neural Information Processing Systems. 2011, 801–809
17 N Craswell, W B Croft, J Guo, B Mitra, M de Rijke. Neu-IR: the SIGIR 2016 workshop on neural information retrieval. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2016, 1245–1246
https://doi.org/10.1145/2911451.2917762
18 N Craswell, W B Croft, M de Rijke, J Guo, B Mitra. SIGIR 2017 workshop on neural information retrieval (Neu-Ir’17). In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 1431–1432
https://doi.org/10.1145/3077136.3084373
19 I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, A Courville, Y Bengio. Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems. 2014, 2672–2680
20 V Mnih, K Kavukcuoglu, D Silver, A A Rusu, J Veness, M G Bellemare, A Graves, M Riedmiller, A K Fidjeland, G Ostrovski, S Petersen, C Beattie, A Sadik, I Antonoglou, H King, D Kumaran, D Wierstra, S Legg, D Hassabis. Human-level control through deep reinforcement learning. Nature, 2015, 518(7540): 529–533
https://doi.org/10.1038/nature14236
21 D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, T Hubert, L Baker, M Lai, A Bolton, Y Chen, T Lillicrap, F Hui, L Sifre, G V D Driessche, T Graepel, D Hassabis. Mastering the game of go without human knowledge. Nature, 2017, 550(7676): 354
https://doi.org/10.1038/nature24270
22 J Wang, L Yu, W Zhang, Y Gong, Y Xu, B Wang, P Zhang, D Zhang. Irgan: a minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 515–524
https://doi.org/10.1145/3077136.3080786
23 E Agichtein, E Brill, S Dumais. Improving web search ranking by incorporating user behavior information. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 19–26
https://doi.org/10.1145/1148170.1148177
24 L A Granka, T Joachims, G Gay. Eye-tracking analysis of user behavior in www search. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004, 478–479
https://doi.org/10.1145/1008992.1009079
25 M R Morris, J Teevan, K Panovich. What do people ask their social networks, and why?: a survey study of status message q&a behavior. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2010, 1739–1748
https://doi.org/10.1145/1753326.1753587
26 W B Croft, S Cronen-Townsend, V Lavrenko. Relevance feedback and personalization: a language modeling perspective. In: Proceedings of the 2nd DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries. 2001
27 B Thomee, M S Lew. Interactive search in image retrieval: a survey. International Journal of Multimedia Information Retrieval, 2012, 1(2): 71–86
https://doi.org/10.1007/s13735-012-0014-4
28 A Said, B J Jain, S Narr, T Plumbaum. Users and noise: the magic barrier of recommender systems. In: Proceedings of International Conference on User Modeling, Adaptation, and Personalization. 2012, 237–248
https://doi.org/10.1007/978-3-642-31454-4_20
29 M Swan. Blockchain: Blueprint for a New Economy. O’Reilly Media, Inc., 2015
30 I F Akyildiz, Ö B Akan, C Chen, J Fang, W Su. Interplanetary internet: state-of-the-art and research challenges. Computer Networks, 2003, 43(2): 75–112
https://doi.org/10.1016/S1389-1286(03)00345-1
31 B M Lavanya. Blockchain technology beyond bitcoin: an overview. International Journal of Computer Science and Mobile Applications, 2018, 6(1): 76–80
32 S Seebacher, R Schüritz. Blockchain technology as an enabler of service systems: a structured literature review. In: Proceedings of International Conference on Exploring Services Science. 2017, 12–23
https://doi.org/10.1007/978-3-319-56925-3_2
33 W B Croft, D Metzler, T Strohman. Search Engines: Information Retrieval in Practice. Addison-Wesley Reading, 2010
34 E M Voorhees, D K Harman. TREC: Experiment and Evaluation in Information Retrieval. Cambridge: MIT Press, 2005
35 D Kelly. Methods for evaluating interactive information retrieval systems with users. Foundations and Trends® in Information Retrieval, 2009, 3(1–2): 1–224
https://doi.org/10.1561/1500000012
36 D Ellis. Theory and explanation in information retrieval research. Journal of Information Science, 1984, 8(1): 25–38
https://doi.org/10.1177/016555158400800105
37 P Vakkari, K Järvelin. Explanation in information seeking and retrieval. New Directions in Cognitive Information Retrieval, 2006, 19: 113–138
https://doi.org/10.1007/1-4020-4014-8_7
38 J Singh, A Anand. EXS: explainable search using local model agnostic interpretability. In: Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 2019, 770–773
https://doi.org/10.1145/3289600.3290620
39 G Luo, C Tang, H Yang, X Wei. Medsearch: a specialized search engine for medical information retrieval. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. 2008, 143–152
https://doi.org/10.1145/1458082.1458104
40 P S Huang, X He, J Gao, L Deng, A Acero, L Heck. Learning deep structured semantic models for Web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. 2013, 2333–2338
https://doi.org/10.1145/2505515.2505665
41 J Guo, Y Fan, Q Ai, W B Croft. A deep relevance matching model for ad-hoc retrieval. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 2016, 55–64
https://doi.org/10.1145/2983323.2983769
42 Y Zhang, M M Rahman, A Braylan, B Dang, H L Chang, H Kim, Q Mc- Namara, A Angert, E Banner, V Khetan, T McDonnell, A T Nguyen, D Xu, B C Wallace, M Leasey. Neural information retrieval: a literature review. 2016, arXiv preprint arXiv:1611.06792
43 B Mitra, N Craswell. Neural models for information retrieval. 2017, arXiv preprint arXiv:1705.01509
https://doi.org/10.1145/3018661.3022755
44 J Guo, Y Fan, L Pang, L Yang, Q Ai, H Zamani, C Wu, WB Croft, X Cheng. A deep look into neural ranking models for information retrieval. 2019, arXiv preprint arXiv:1903.06902
https://doi.org/10.1016/j.ipm.2019.102067
45 D Sharma, S Kumar, C Kholia. Multi-modal information retrieval system. US Patent 7,054,818, 2006
46 D Lee, J Park, J H Ahn. On the explanation of factors affecting ecommerce adoption. In: Proceedings of the International Conference on Information Systems. 2001, 109–120
47 M Jamali, M Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems. 2010, 135–142
https://doi.org/10.1145/1864708.1864736
48 C Callison-Burch. Fast, cheap, and creative: evaluating translation quality using amazon’s mechanical turk. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. 2009, 286–295
https://doi.org/10.3115/1699510.1699548
49 J Gubbi, R Buyya, S Marusic, M Palaniswami. Internet of Things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems, 2013, 29(7): 1645–1660
https://doi.org/10.1016/j.future.2013.01.010
50 M Abadi, P Barham, J Chen, Z Chen, A Davis, J Dean, M Devin, S Ghemawat, G Irving, M Isard, M Kudlur, J Levenberg, R Monga, S Moore, D G Murray, B Steiner, P Tucker, V Vasudevan, P Warden, M Wicke, Y Yu, X Zheng. Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation. 2016, 265–283
51 Y Jia, E Shelhamer, J Donahue, S Karayev, J Long, R Girshick, S Guadarrama, T Darrell. Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia. 2014, 675–678
https://doi.org/10.1145/2647868.2654889
52 A Paszke, S Gross, S Chintala, G Chanan. Pytorch: tensors and dynamic neural networks in python with strong GPU acceleration. 2017
53 M McCandless, E Hatcher, O Gospodnetic. Lucene in Action: Covers Apache Lucene 3.0. Greenwich, CT: Manning Publications Co., 2010
[1] Article highlights Download
Related articles from Frontiers Journals
[1] Chuan SHI, Jiayu DING, Xiaohuan CAO, Linmei HU, Bin WU, Xiaoli LI. Entity set expansion in knowledge graph: a heterogeneous information network perspective[J]. Front. Comput. Sci., 2021, 15(1): 151307.
[2] Zhenghui HU, Wenjun WU, Jie LUO, Xin WANG, Boshu LI. Quality assessment in competition-based software crowdsourcing[J]. Front. Comput. Sci., 2020, 14(6): 146207.
[3] Chunxi ZHANG, Yuming LI, Rong ZHANG, Weining QIAN, Aoying ZHOU. Benchmarking on intensive transaction processing[J]. Front. Comput. Sci., 2020, 14(5): 145204.
[4] Yi LIU, Tian SONG, Lejian LIAO. TPII: tracking personally identifiable information via user behaviors in HTTP traffic[J]. Front. Comput. Sci., 2020, 14(3): 143801.
[5] Lu LIU, Shang WANG. Meta-path-based outlier detection in heterogeneous information network[J]. Front. Comput. Sci., 2020, 14(2): 388-403.
[6] Samuel IRVING, Bin LI, Shaoming CHEN, Lu PENG, Weihua ZHANG, Lide DUAN. Computer comparisons in the presence of performance variation[J]. Front. Comput. Sci., 2020, 14(1): 21-41.
[7] Kai LI, Guangyi LV, Zhefeng WANG, Qi LIU, Enhong CHEN, Lisheng QIAO. Understanding the mechanism of social tie in the propagation process of social network with communication channel[J]. Front. Comput. Sci., 2019, 13(6): 1296-1308.
[8] Farid FEYZI, Saeed PARSA. Inforence: effective fault localization based on information-theoretic analysis and statistical causal inference[J]. Front. Comput. Sci., 2019, 13(4): 735-759.
[9] Peng PENG, Lei ZOU, Zhenqin DU, Dongyan ZHAO. Using partial evaluation in holistic subgraph search[J]. Front. Comput. Sci., 2018, 12(5): 966-983.
[10] Fei YAN, Sihao JIAO, Abdullah M. ILIYASU, Zhengang JIANG. Chromatic framework for quantum movies and applications in creating montages[J]. Front. Comput. Sci., 2018, 12(4): 736-748.
[11] Shaobo DENG, Sujie GUAN, Min LI, Lei WANG, Yuefei SUI. Decomposition for a new kind of imprecise information system[J]. Front. Comput. Sci., 2018, 12(2): 376-395.
[12] Zhilin LI, Wenbo XU, Xiaobo ZHANG, Jiaru LIN. A survey on one-bit compressed sensing: theory and applications[J]. Front. Comput. Sci., 2018, 12(2): 217-230.
[13] Ilyes KHENNAK, Habiba DRIAS. Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE[J]. Front. Comput. Sci., 2018, 12(1): 163-176.
[14] Jihong YAN, Chengyu WANG, Wenliang CHENG, Ming GAO, Aoying ZHOU. A retrospective of knowledge graphs[J]. Front. Comput. Sci., 2018, 12(1): 55-74.
[15] Houkui ZHOU, Huimin YU, Roland HU. Topic evolution based on the probabilistic topic model: a review[J]. Front. Comput. Sci., 2017, 11(5): 786-802.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed