Supporting Users Tasks with Personal Information Management and Web Forms Augmentation

Firmenich, Sérgio; Gaits, Vincent; Gordillo, Silvia; Rossi, Gustavo; Winckler, Marco

doi:10.1007/978-3-642-31753-8_20

Cited by 14 publications

(17 citation statements)

References 21 publications

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“…They use four sources, such as random values, to automatically assign values for input parameters of web services. Firmenich et al [28] analyze user's interactions with web forms and enhance web forms to assist users to fill in web forms. Wang et al [29] propose a classification of user inputs and use the classification to help pre-fill web forms using an end-user's previous inputs.…”

Section: Related Workmentioning

confidence: 99%

Automatic Reuse of User Inputs to Services among End-Users in Service Composition

Wang

Zou

Keivanloo

et al. 2015

IEEE Trans. Serv. Comput.

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End-users conduct various on-line activities. Quite often, they re-visit websites and use services to perform re-occurring activities, such as on-line shopping. The end-users are required to enter the same information into various web services to accomplish such re-occurring tasks. It can negatively impact user experience when a user needs to type the re-occurring information repetitively into such web services. In this paper, we propose an approach to prevent end-users from performing such repetitive tasks. Our approach propagates user inputs across services by linking similar input and output parameters. Our approach pre-fills values to the input parameters for an end-user using his or her previous inputs. To increase the chance of identifying a proper value for an input parameter performed by one end-user, our approach also leverages the inputs from other end-users. We identify and link similar end-users to enable the propagation of user inputs among end-users. We have designed and developed a prototype. We also conduct an empirical study to evaluate our approach using the real world services. The empirical results show that our approach using an end-user's previous inputs can reduce on average 41% of repetitive typing for the execution of composed services. Furthermore, the previous inputs from the similar end-users can improve our approach in reducing the repetitive typing for an end-user.

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Section: Related Workmentioning

confidence: 99%

Automatic Reuse of User Inputs to Services among End-Users in Service Composition

Wang

Zou

Keivanloo

et al. 2015

IEEE Trans. Serv. Comput.

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“…Collecting accurate user Geo-location data is a difficult task. We automatically collect user's current location from web browsers using Google Geo-location Maps Javascript API 8 and parse it into contexts at different levels of granularity. To know the type and name of a current user location, we allow a user to label the location.…”

Section: Collecting User Inputs and Their Contextsmentioning

confidence: 99%

“…We store all previous user inputs in the proposed contextaware meta-data model (Section 3.1.1). To help a user fill in 8. https://developers.google.com/maps/documentation /javascript/examples/map-geolocation 9. http://www.json.org/ This is the author's version of an article that has been published in this journal.…”

Section: Recommending Values To Users For Filling In Input Parametersmentioning

confidence: 99%

“…To test a category of features, we first exclude the tested category of features from the training dataset auto-data. Second, we apply RanKBoost on the autodata and use Equation 7and Equation (8) to calculate the average precision and recall for RanKBoost. We test 6 categories of features and compare the results with the ones of RQ1 to identify the effect of different features on ranking.…”

Section: Rq 3 Which Ranking Features Affect the Ranking Most?mentioning

confidence: 99%

“…Recently, several approaches (e.g., [3] [6] [7] [8] [9]) have been developed to aid users filling out on-line services using previous inputs. The research mainly proposes two types of techniques: (1) Pre-filling user inputs to input parameters before users start typing (e.g., [7] [8] [6]); and (2) Recommending values to input parameters for users (e.g., [3] [9]). However, the existing approaches mainly suffer the following limitations:…”

Section: Introductionmentioning

confidence: 99%

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Context-aware Service Input Ranking by Learning from Historical Information

Wang

Zou

et al. 2017

IEEE Trans. Serv. Comput.

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Users visit on-line services and compose them to accomplish on-line tasks, such as shopping on-line. Quite often, users enter the same information into various on-line services to finish on-line tasks. However, repetitively typing the same information into web forms is a tedious job for users. In this paper, we propose a context-aware ranking framework to rank values for input parameters. We propose 6 categories of ranking features constructed from various types of information, such as user contexts and patterns of user inputs. Our framework adopts learning-to-rank (LtR) algorithms that consist of a set of machine learned models to automatically construct ranking models by integrating the ranking features. When a user enters a value to an input parameter, an interaction between the user input and the input parameter is established. Our framework learns information relevant to such interactions and ranks user inputs in different contexts. Through empirical studies on the real-world on-line services, we obtain the following main results: (1) Among the 8 state-of-the-art learning-to-rank models, RankBoost can outperform other LtR models on ranking user inputs for input parameters; (2) Our framework using IRSVM that performs the worst among the LtR models outperforms the two baseline conventional ranking models and Google Chrome Autofilling, an industrial tool, on ranking user inputs to input parameters; and (3) We observe that the textual information of user inputs and input parameters is the most influential factor on ranking user inputs. Among the various types of contextual data, user locations and time matter the most to the ranking of user inputs.

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