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Modeling and predicting the user next input by Bayesian reasoning

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Abstract

We focus on enhancing the user experience by predicting entries when a form is filled, according to past interactions. The purpose of having a predictive model of form filling is to reduce the amount of time required to fill a form, and thus to reduce the fatigue and repetitiveness associated to this common task. Generally predictive models ignore the values entered by users in the other fields in the form, and just focus on the value getting entered at the current field. This is a limit to the model capabilities. Instead, we are aimed at predicting the sequence of entries in a form, instead of the value of single fields in isolation. This is done by means of inference over a Bayesian network, able to compute the a posteriori probability that remaining fields will assume certain values, given the set of values entered so far. The model structure and parameters can be learned from a dataset of past entries. The paper investigates computational and convergence issues under both the closed world assumption and the open world assumptions. As case study, we considered forms used for online payment of money order used at Poste Italiane, and we exploited this approach to prototype two different solutions for desktop and mobile applications. Results of experimentation with a user test group prove the proposed approach is able to provide an effective and appreciated support in filling a form.

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Notes

  1. It is a theoretical limit as it does not take into account typing errors, control strokes (e.g., Alt, Tab, Ctrl, etc.) and the cognitive overhead in assuming a prediction.

  2. This is also related to the definition of keystroke savings as it does not consider the effect of wrong predictions entailing keystrokes for deleting mistakes.

References

  • Bérard C, Niemeijer D (2004) Evaluating effort reduction through different word prediction systems. SMC 3:2658–2663

    Google Scholar 

  • Bickel S, Haider P, Scheffer T (2005) Predicting sentences using n-gram language models. In: HLT ’05: proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, Morristown, pp 193–200

  • Bonino D, Corno F, Squillero G (2003) Dynamic prediction of web requests. In: Sarker R, Reynolds R, Abbass H, Tan KC, McKay B, Essam D, Gedeon T (eds) Proceedings of the 2003 Congress on evolutionary computation CEC2003. IEEE Press, Canberra, pp 2034–2041

  • Box GEP, Tiao GC (1992) Bayesian inference in statistical analysis. Wiley Classics Library edn. Wiley-Interscience, New York

  • Card SK, Moran TP, Newell A (1980) The keystroke-level model for user performance time with interactive systems. Commun ACM 23(7):396–410. doi:10.1145/358886.358895

  • Davison BD (2002) Predicting web actions from html content. In: Proceedings of the 13th ACM conference on hypertext and hypermedia (HT’02), College Park, MD, pp 159–168

  • Davison BD (2004) Learning web request patterns. In: Web dynamics, pp 435–460

  • Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol) 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  • Dong Y, Li Q (2012) A deep web crawling approach based on query harvest model. J Comput Inf Syst 8(3):973–981

    Google Scholar 

  • Dongshan X, Junyi S (2002) A new Markov model for web access prediction. Comput Sci Eng 4(6):34–39

    Article  Google Scholar 

  • Firmenich S, Gaits V, Gordillo S, Rossi G, Winckler M (2012) Supporting users tasks with personal information management and web forms augmentation. In: Proceedings of the 12th international conference on web engineering. ICWE’12. Springer, Berlin, pp 268–282. doi:10.1007/978-3-642-31753-8_20

  • Fitchett S, Cockburn A (2012) AccessRank: predicting what users will do next. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, USA, pp 2239–2242

  • Furche T, Gottlob G, Grasso G, Guo X, Orsi G, Schallhart C (2013) The ontological key: automatically understanding and integrating forms to access the deep web. VLDB J 22(5):615–640. doi:10.1007/s00778-013-0323-0

    Article  Google Scholar 

  • Grabski K, Scheffer T (2004) Sentence completion. In: SIGIR ’04: proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp. 433–439. doi:10.1145/1008992.1009066

  • Guo X, Kranzdorf J, Furche T, Grasso G, Orsi G, Schallhart C (2012) Opal: a passe-partout for web forms. In: Proceedings of the 21st international conference on World Wide Web. WWW ’12 companion. ACM, New York, pp 353–356. doi:10.1145/2187980.2188047

  • Ipeirotis PG, Gravano L, Sahami M (2001) Probe, count, and classify: categorizing hidden web databases. In: Proceedings of the 2001 ACM SIGMOD international conference on management of data. SIGMOD ’01. ACM, New York, pp 67–78. doi:10.1145/375663.375671

  • Jiang L, Wu Z, Zheng Q, Liu J (2009) Learning deep web crawling with diverse features. In: Proceedings of the 2009 IEEE/WIC/ACM international joint conference on Web intelligence and intelligent agent technology, vol 01. WI-IAT ’09. IEEE Computer Society, Washington, DC, pp 572–575. doi:10.1109/WI-IAT.2009.96

  • Jiang L, Wu Z, Feng Q, Liu J, Zheng Q (2010) Efficient deep web crawling using reinforcement learning. In: Proceedings of the 14th Pacific-Asia conference on advances in knowledge discovery and data mining—volume Part I, PAKDD’10. Springer, Berlin, pp 428–439. doi:10.1007/978-3-642-13657-3_46

  • Kantorski GZ, Moreira VP, Heuser CA (2015) Automatic filling of hidden web forms: a survey. SIGMOD Rec 44(1):24–35. doi:10.1145/2783888.2783898

    Article  Google Scholar 

  • Khosravi M, Tarokh M (2010) Dynamic mining of users interest navigation patterns using naive Bayesian method. In: 2010 IEEE international conference on intelligent computer communication and processing (ICCP), pp 119–122. doi:10.1109/ICCP.2010.5606453

  • Lage JP, da Silva AS, Golgher PB, Laender AHF (2004) Automatic generation of agents for collecting hidden web pages for data extraction. Data Knowl Eng 49(2):177–196. doi:10.1016/j.datak.2003.10.003

    Article  Google Scholar 

  • Liddle S, Embley D, Scott D, Yau S (2003) Extracting data behind web forms. In: Oliv A, Yoshikawa M, Yu E (eds) Advanced conceptual modeling techniques. Lecture notes in computer science, vol 2784. Springer, Berlin, pp 402–413. doi:10.1007/978-3-540-45275-1_35

  • Lin K, Wang CJ, Chen HH (2011) Predicting next search actions with search engine query logs. In: 2011 IEEE/WIC/ACM international conference on Web intelligence and intelligent agent technology (WI-IAT), vol 1, pp 227–234. doi:10.1109/WI-IAT.2011.15

  • Nandi A, Jagadish HV (2007) Effective phrase prediction. In: VLDB ’07: proceedings of the 33rd international conference on very large data bases. VLDB Endowment, Vienna, pp 219–230

  • Nanopoulos A, Nanopoulos R, Katsaros D, Manolopoulos Y, Society IC (2002) A data mining algorithm for generalized web prefetching. IEEE Trans Knowl Data Eng 15:1155–1169

    Google Scholar 

  • Raghavan S, Garcia-Molina H (2001) Crawling the hidden web. In: Proceedings of the 27th international conference on very large data bases. VLDB ’01. Morgan Kaufmann Publishers Inc., San Francisco, pp 129–138. http://dl.acm.org/citation.cfm?id=645927.672025

  • Rukzio E, Noda C, De Luca A, Hamard J, Coskun F (2008) Automatic form filling on mobile devices. Pervasive Mob Comput 4(2):161–181. doi:10.1016/j.pmcj.2007.09.001

    Article  Google Scholar 

  • Russo G, Birtolo C, Troiano L (2008) Generative UI design in SAPI project. In: CHI ’08 extended abstracts on human factors in computing systems. CHI EA ’08. ACM, New York, pp 3627–3632. doi:10.1145/1358628.1358903

  • Soulemane M, Rafiuzzaman M, Mahmud H (2012) Article: Crawling the hidden web: an approach to dynamic web indexing. Int J Comput Appl 55(1):7–15 (Full text available)

  • Steck H (2001) Constraint-based structural learning in Bayesian networks using finite data sets. PhD thesis, Department of Informatics, Technical University Munich, Munich, Germany

  • Steck H, Tresp V (1999) Bayesian belief networks for data mining. In: Proceedings of the 2nd workshop on data mining und data warehousing als Grundlage moderner entscheidungsunter-stuetzender Systeme, pp 145–154

  • Su Z, Yang Q, Lu Y, Zhang H (2000) Whatnext: a prediction system for web requests using n-gram sequence models. In: International conference on Web information systems engineering, vol 1, p 0214. doi:10.1109/WISE.2000.882395

  • Toda GA, Cortez E, Mesquita F, da Silva AS, Moura E, Neubert M (2009) Automatically filling form-based web interfaces with free text inputs. In: Proceedings of the 18th international conference on World Wide Web. WWW ’09. ACM, New York, pp 1163–1164. doi:10.1145/1526709.1526908

  • Toda GA, Cortez E, da Silva AS, de Moura E (2010) A probabilistic approach for automatically filling form-based web interfaces. Proc VLDB Endow 4(3):151–160. doi:10.14778/1929861.1929862

    Article  Google Scholar 

  • Trnka K, McCaw J, Yarrington D, McCoy KF, Pennington C (2009) User interaction with word prediction: the effects of prediction quality. ACM Trans Access Comput 1:17:1–17:34

  • Troiano L, Birtolo C (2014) Genetic algorithms supporting generative design of user interfaces: examples. Inf Sci 259:433–451. doi:10.1016/j.ins.2012.01.006

    Article  Google Scholar 

  • Troiano L, Birtolo C, Miranda M (2008) Adapting palettes to color vision deficiencies by genetic algorithm. In: Proceedings of the 10th annual conference on genetic and evolutionary computation. GECCO ’08. ACM, New York, pp 1065–1072. doi:10.1145/1389095.1389291

  • Troiano L, Cirillo G, Armenise R, Birtolo C (2009) A preliminary experience in optimizing the layout of web pages by genetic algorithms to fit mobile devices. In: Proceedings of the 2009 ninth international conference on intelligent systems design and applications, ISDA ’09. IEEE Computer Society, Washington, DC, pp 1055–1061. doi:10.1109/ISDA.2009.53

  • W3C (2008) Mobile web best practices 1.0. Technical report, W3C Recommendation. http://www.w3.org/TR/mobile-bp/

  • Winckler M, Gaits V, Vo DB, Sergio F, Rossi G (2011) An approach and tool support for assisting users to fill-in web forms with personal information. In: Proceedings of the 29th ACM international conference on design of communication. SIGDOC ’11. ACM, New York, pp 195–202. doi:10.1145/2038476.2038515

  • Wroblewski L (2008) Web form design: filling in the blanks. Louis Rosenfeld, Brooklyn

    Google Scholar 

  • Wu P, Wen JR, Liu H, Ma WY (2006) Query selection techniques for efficient crawling of structured web sources. In: Proceedings of the 22nd international conference on data engineering. ICDE ’06. IEEE Computer Society, Washington, DC, p 47. doi:10.1109/ICDE.2006.124

  • Zhu J, Hong J, Hughes JG (2002) Using Markov chains for link prediction in adaptive web sites. In: Soft-ware 2002: proceedings, of the 1st international conference on computing in an imperfect world. Springer, London, pp 60–73

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Authors and Affiliations

  1. Department of Engineering, University of Sannio, Viale Traiano, 82100, Benevento, Italy

    Luigi Troiano

  2. Poste Italiane S.p.A., SI/CCUP/GC, p.zza Matteotti 3, 80133, Naples, Italy

    Cosimo Birtolo & Roberto Armenise

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  1. Luigi Troiano
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  2. Cosimo Birtolo
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  3. Roberto Armenise
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Correspondence to Luigi Troiano.

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Communicated by V. Loia.

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Troiano, L., Birtolo, C. & Armenise, R. Modeling and predicting the user next input by Bayesian reasoning. Soft Comput 21, 1583–1600 (2017). https://doi.org/10.1007/s00500-015-1870-7

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