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As to the reformulation process of Elsevier system, there are several drawbacks to be improved. Firstly, the query expansion function only exists associated to the results rather than the queries themselves. In other words, the relationship that IR system built is merely connected by the existing resources but not the potential results which users are searching for or interested in (Soo Young Rieh et al, 2006). Thus, this kind of experience won’t be widely accepted by lots users, especially the professional ones with specific purposes. As a consequence, the reformulation process towards the query expansion function built between queries and resources should be improved to meet the requirements of the users. In addition, another problem regarding to the reformulation process also exists as analyzed above. The system cannot present related queries or topics when unsuccessful queries are input at the beginning, which are able to bring much trouble for the users to proceed with their search just like the lack of misspelling correction. At this point, Google has to be mentioned as a positive model again. The combination of misspelling correction function with the related queries recommendations has been wonderful performed in google scholar search engine, which realize the perfect application of reformulation process, especially the query expansion. For the designers of Elsevier system, the misspelled correction technology together with eye-tracking technology (adopt three-line paragraph) may be helpful to optimize the functions (Cutrell and Guan, 2007). All in all, Elsevier is a qualified Information Retrieval system with lots of merits that facilitate the users except some technological drawbacks to make it more efficient and effective in the future.
References:
CD Manning et al. (2009). Information Retrieval System. Cambridge University Press. Available online: http://www.langtoninfo.co.uk
Cucerzan and Brill. (2004), Spelling Correction As An Iterative Process That Exploits The Collective Knowledge Of Web Users.Proceedings Of Conference On Empirical Methods In Natural Language Processing (Emnlp'04), pages 293–300.
Duan.H. (2011), Online spelling correctionfor query completion. Proceedings of the 20thinternational conference on World wide web, pages 117–126.
Hargittai. (2006), Hurdles to information seeking: Spelling and typographical mistakes during users' online behavior. Journal of the Association for Information Systems, 7(1):52–67.
M. Hearst. (2006). Design Recommendations for Hierarchical Faceted Search Interfaces, ACM SIGIR workshop on faceted search. Avialable online: http://flamenco.sims.berkeley.edu
Olga Vechtomova. (2006). A study of the effect of term proximity on query expansion. Journal of Information Science,32(4): 324-333.
Ricardo & Berthier. (1999). Modern Information Retrieval. ACM press. Available online: ftp://mail.im.tku.edu.tw
Ruthven. (2008). Interactive information retrieval. Annual Review of Information Science and Techonololy, 42(1): 43-91.
Ryen W. White et al. (2006) An implicit feedback approach for interactive information retrieval.Information Processing & Management, 42(1): 166-190.
S. Cucerzan and E. Brill. Spelling correction as an iterativeprocess that exploits the collective knowledge of web users.
Soo Young Rieh et al. (2006). Analysis of multiple query reformulations on the web: The interactive information retrieval context. Information Processing & Management, 42 (3): 751-768.
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