Modeling and predicting the user next input by Bayesian reasoning

Troiano, Luigi; Birtolo, Cosimo; Armenise, Roberto

doi:10.1007/s00500-015-1870-7

Cited by 10 publications

(11 citation statements)

References 34 publications

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“…Several automated form filling approaches have been proposed [52,37,70]; the basic idea is to mine dependencies among fields from the values recorded in previous form filling sessions, to build recommendation models. These models can then be used to suggest possible values on a target field based on the filled fields in the current form.…”

Section: Challenges Of Automated Form Fillingmentioning

confidence: 99%

“…However, this assumption is unrealistic from a practical standpoint. Although some approaches [52,70] are insensitive to the form filling order (e.g., suggesting possible values of a target field based on the frequency of values in historical inputs), they may not provide accurate suggestions due to their limitations in accurately mining dependencies among fields (as discussed in section 5.2). Hence, one of the challenges in automated form filling is how to build recommendation models (e.g., by mining dependencies among fields) without making any assumption on the order in which fields are filled.…”

Section: Challenges Of Automated Form Fillingmentioning

confidence: 99%

“…Hermens and Shlimmer [37] applied decision tree and hierarchical clustering for filling forms in an electronic leave report system: the study reports that these algorithms performed worse than the simple most-frequent method when the forms were filled out in a random order. Troiano et al [70] trained a Bayesian network model on the historical inputs from a user for an online payment system. The trained model could auto-fill the payment form for the same user by reusing his historical payment records.…”

Section: Related Workmentioning

confidence: 99%

“…The filling order of fields influences the ability of form filling algorithms [37]. Several works refactor data entry forms to provide effective supports for form filling, for example using fields dependencies [17,70] or user roles [2]. All these approaches identify the fields that a target field directly depends on and change their (field) order so that they can be filled before the target field, to increase the accuracy of predicting the latter.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

Belgacem,

Li,

Bianculli

et al. 2022

Preprint

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Users frequently interact with software systems through data entry forms. However, form filling is time-consuming and error-prone. Although several techniques have been proposed to auto-complete or pre-fill fields in the forms, they provide limited support to help users fill categorical fields, i.e., fields that require users to choose the right value among a large set of options.In this paper, we propose LAFF, a learning-based automated approach for filling categorical fields in data entry forms. LAFF first builds Bayesian Network models by learning field dependencies from a set of historical input instances, representing the values of the fields that have been filled in the past. To improve its learning ability, LAFF uses local modeling to effectively mine the local dependencies of fields in a cluster of input instances. During the form filling phase, LAFF uses such models to predict possible values of a target field, based on the values in the already-filled fields of the form and their dependencies; the predicted values (endorsed based on field dependencies and prediction confidence) are then provided to the end-user as a list of suggestions.We evaluated LAFF by assessing its effectiveness and efficiency in form filling on two datasets, one of them proprietary from the banking domain. Experimental results show that LAFF is able to provide accurate suggestions with a Mean Reciprocal Rank value above 0.73. Furthermore, LAFF is efficient, requiring at most 317 ms per suggestion.

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Section: Challenges Of Automated Form Fillingmentioning

confidence: 99%

Section: Challenges Of Automated Form Fillingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

Belgacem,

Li,

Bianculli

et al. 2022

Preprint

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show abstract

“…In [6], authors have described a predictive form fill-up system depending on the past history of the user so that the amount of time required to fill the form is reduced, model tries to predict sequence of entries of a form not only the current filed entry.…”

Section: Related Workmentioning

confidence: 99%

A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

2019

ijeat

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Word prediction is a technique which tries to suggest the users’ words after knowing the few input letters of the user. This predictive model also tries to generate the future words or next words of a sentence by observing earlier words of the sentence. In this research, two problems are combined, one is word prediction and the next is handling of ambiguous words. A word prediction model predicts the future words of a sentence by using n-gram based model. In general, predictive models use unigram, bigram or trigram models to predict the next words. In case of sentences consisting of ambiguous words, the predictive model by using only bigram or trigram cannot perform well to predict the next words. To enhance this prediction for ambiguous words, maximum of six previous input words are observed and try to predict almost the exact words after the ambiguous words in those particular contexts. Different level of experiments are done and the results are compared for modified or enhanced prediction model with the traditional prediction model, improvement on accuracy and failure rate are found in the enhanced model. The accuracy of the Traditional Model is 60.68% on the hand the accuracy of the Enhanced Model is 66.88%. The failure rate of the Traditional Model is 32.35% and the Enhanced Model is 29.17%

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Intelligent and Secure Autofill System in Web Browsers

Bose¹

2021

Advances in Intelligent Systems and Computing

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Modeling and predicting the user next input by Bayesian reasoning

Cited by 10 publications

References 34 publications

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

Intelligent and Secure Autofill System in Web Browsers

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