A method to support dynamic domain model based on user interests for effective language learning

Author

Quijano, Isabella Pauline ; Junshean Espinosa, Kurt ; Troussas, C.

Author_Institution

Dept. of Comput. Sci., Univ. of the Philippines Cebu, Lahug, Philippines

fYear

2014

fDate

7-9 July 2014

Firstpage

322

Lastpage

325

Abstract

This study aims to explore a method that can generate a dynamic domain model based on the user´s interests and status updates. To get the most relevant interests of an individual the following algorithms were used after the study by M. Timonen: Inverse Fragment Length, Category Probability, Binormal Separation, Fragment Length Weighted Category Distribution and Time Sensitive Term Weighting. This study has shown that it is possible to obtain a dynamic user model representation through their social media profile. This was done by implementing a proof-of-concept application on news recommender system. Future work for this study includes evaluating this method in language learning.

Keywords

data mining; learning (artificial intelligence); natural language processing; probability; social networking (online); statistical distributions; text analysis; binormal separation; category probability; dynamic domain model; dynamic user model representation; effective language learning; fragment length weighted category distribution; inverse fragment length; machine learning; news recommender system; social media profile; text mining approach; time sensitive term weighting; user interests; Androids; Computational modeling; Feeds; Humanoid robots; Java; Media; Text mining; ITS; TF-IDF; domain model;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Intelligence, Systems and Applications, IISA 2014, The 5th International Conference on

Conference_Location

Chania

Type

conf

DOI

10.1109/IISA.2014.6878766

Filename

6878766