Abstract
The paper presents one of the main modules of HAMIS recommender system built for 34 business companies (clients) involved in heavy equipment repair in the US and Canada. This module is responsible for meta-actions discovery from a large collection of comments, written as text, collected from customers about their satisfaction with services provided by each client. Meta-actions, when executed, trigger action rules discovered from customers data which are in a table format. We specifically focus on the process of mining meta-actions, which consists of four representative and characteristic steps involving sentiment analysis and text summarization. Arranging these four steps in proposed order distinguishes our work from others and better serves our purpose. Compared to procedures presented in other works, each step in our procedure is adapted accordingly with respect to our own observations and knowledge of the domain. Results obtained from the experiments prove the high effectiveness of the proposed approach for mining meta-actions.
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Kuang, J., Raś, Z.W., Daniel, A. (2015). Personalized Meta-Action Mining for NPS Improvement. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_9
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DOI: https://doi.org/10.1007/978-3-319-25252-0_9
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