Bringing knowledge into recommendation systems

Tomaz, Luiz F. C.; Nt, José A. Rodrigues; Souza, Jano Moreira de; Xexéo, Geraldo

doi:10.1109/cscwd.2011.5960081

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…Details of traces are showed in Table II. According to (3) and (4), we obtain: Likewise, we obtain the index of user Ala on subject "WP" . With the traces of two users and other users in the same group, we can conclude that "Ala" is less competent than "Ning" on "WP" relying on…”

Section: Examplementioning

confidence: 99%

“…A set of actions, step by step, is defined as a trace by Zarka et al [2]. Under careful modelling and analysis, traces could in return help indicating the competency of an individual or a group of users as shown by Tomaz et al [3]. Thus, with the information exploited behind traces, we can improve collaboration, as mentioned by Garcia-Crespo et al focusing on the reuse of traces for different purposes such as decision aid [4] or by Chang et al for recommendation [5].…”

Section: Introductionmentioning

confidence: 99%

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Mining user competency from semantic trace

Wang

Abel

Barthès

et al. 2015

2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Abstract-In order to achieve individual or collective goals, users in informational environments collaborate to integrate intellectual resources and knowledge. Thanks to informational environments, users can better organize, realize and record collaboration. Every activity produces a set of traces. Such traces can be recorded and classified, based on a model of traces. With the help of a model of competency, these traces also contribute to evaluate the competency of users on certain subjects. In this article, we propose a semantic model of traces and analyze classified traces by means of TF-IDF. We also considered the impact of time on the decreasing importance of traces. We show how to offer users recommendations and decision aid.

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Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining user competency from semantic trace

Wang

Abel

Barthès

et al. 2015

2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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“…When the recommendations are based on user evaluations, as seen in [13] and [14], it is necessary to create mechanisms to reward users that give a true evaluation. In [15] and [16] this is discussed and a mechanism to solve this problem, the HYRIWYG, is proposed.…”

Section: B Recommender Systemsmentioning

confidence: 99%

“…Nowadays, recommender systems are widely used: from systems that support software engineering tasks like [14] [17], systems that recommend pages to replace broken web links [18], to selecting the most appropriate advertisement for a certain customer [19].…”

Section: B Recommender Systemsmentioning

confidence: 99%

A recommender system for metrics

Tomaz

Souza

et al. 2012

Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Over the past 5 decades society has been increasingly requiring immediate results, and this is reflected in business. Due to this, the attention given to strategy, strategy management and strategy planning has increased. The use of the Balanced Scorecard (BSC) is a way of achieving this strategic capability. Moreover, a proper BSC has effective metrics to track its goals. This work proposes a system that recommends metrics in accordance with the goals of a BSC. The recommendation is made using data mining techniques, in particular association rules.

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“…A set of actions, step by step, is defined as a trace [4]. Under modelling and analysis, traces in return help indicating the competency of an individual [5]. Based on the information exploited from the traces, we improve collaboration focusing on the reuse of traces for different purposes like decision aid and recommendation [6].…”

Section: Introductionmentioning

confidence: 99%

Recommending Competent Users from Semantic Traces Using a Bayes Classifier

Wang

Abel

Barthès

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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When users collaborate, they leave traces in some way or another. These traces in return offer a clue whether a user is competent enough on a subject. This helps further collaboration because knowing the specialization of users helps to distribute tasks reasonably. In this article, we propose a semantic model of traces and analyze classified traces using a Bayes classifier. We exploit the results to offer recommendation on competent users accordingly.

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Bringing knowledge into recommendation systems

Cited by 5 publications

References 20 publications

Mining user competency from semantic trace

Mining user competency from semantic trace

A recommender system for metrics

Recommending Competent Users from Semantic Traces Using a Bayes Classifier

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