Abstract
The personalization presents a key factor in the majority of web application. It improves the user experience by serving each user according to his desires and intentions. To ensure a good personalization process, the evaluation should be considered. However, in the literature, assessing this process is considered as a secondary purpose and treated as a lowcost test before the final deployment of a web application. Besides, the majority of assessments are domain dependent. They also report performance for a short time period and for specific users. Thus, we propose a solution, called RPMAS, designed with an adaptive architecture, based on agents. It is a reference system that attempts to personalize the same services offered by the assessed web application. Then, a comparison is performed to evaluate the results of the concerned web application. Following this comparison, RPMAS proposes improvements to the evaluated web application, or makes a self-evaluation to treat its proper weaknesses. In this article, we expose the detailed architecture of the proposed solution. Indeed, our solution is composed of three layers: the Observation Layer, the Modeling and Data Processing Layer and the Prediction, Recommendation and Evaluation Layer. Each layer has several intelligent and adaptive agents. We underline the various RPMAS advantages, compared to the state of the art. Then, we provide the proof of the efficiency of our contribution when evaluating different web applications. Finally, we developed a first scenario which illustrates the assessment made by our RPMAS regarding an online library. Also, we deploy a second scenario to assess an intelligent tutoring application. Regarding the accuracy measure, the gap between RPMAS and the first application is 17.316%. For the second scenario, using the AUC measure, the gap indicates 13.09%.
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Notes
Please refer on abbreviations to get the meaning of all used acronyms
Abbreviations
- O-Layer:
-
Observation Layer
- MDP-Layer:
-
Modeling and Data Processing Layer
- PRE-Layer:
-
Prediction, Recommendation and Evaluation Layer
- RPMAS:
-
Referential Personalized Multi-Agent System
- ConvLSTM:
-
Convolution Long Short Term Memory
- AIDKVMN:
-
Augmented Input Dynamic Key Value Memory Network
- AUC:
-
Area Under the Curve
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Trifa, A., Hedhili, A. & Chaari, W.L. Adaptive architecture based on agents for assessing a web application. Multimed Tools Appl 81, 40581–40607 (2022). https://doi.org/10.1007/s11042-022-13059-9
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DOI: https://doi.org/10.1007/s11042-022-13059-9