A framework of conversational recommender system based on user functional requirements

2014 ◽  
Author(s):  
Dwi H. Widyantoro ◽  
Z. K. A. Baizal
Keyword(s):  
Recommender System ◽  
Functional Requirements

scholarly journals Conversational Recommender System with Explanation Facility Using Semantic Reasoning

2016 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Zk Abdurahman Baizal ◽  
Nur Rahmawati
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
User Model ◽  
Functional Requirements ◽  
Inference Method ◽  
Semantic Reasoning ◽  
Filtering Method ◽  
Knowledge Based ◽  
The Relationship ◽  
Number Of Iterations

<p>Conversational recommender system is system that provides dialogue as user guide to obtain information from the user, in order to obtain preference for products needed. This research implements conversational recommender system with knowledge-based in the smartphone domain with an explanation facility. The recommended products are obtained based on the functional requirements of the user. Therefore, this study use ontology model as a knowledge to be more flexible in finding products that is suitable with the functional requirements of the user that is by tracing a series of semantic based on relationships in order to obtain the user model. By exploiting the relationship between instances of user models, the explanation facility generated can be more natural. Our filtering method uses semantic reasoning with inference method to avoid overspecialization. The evaluation show that the performance of our recommender system with explanation facilities is more efficient than the recommendation system without explanation facility, that can be seen from the number of iterations. We also notice that our system has accuracy of 84%.</p>

scholarly journals ALGORITHMIC SUPPORT OF THE WEB SERVICE RECOMMENDATION SYSTEM FOR LEARNING FOREIGN LANGUAGES

2021 ◽  
pp. 100-106
Author(s):  
Liliia Bodnar ◽  
Kateryna Shulakova ◽  
Liudmyla Gryzun
Keyword(s):  
Foreign Language ◽  
Web Service ◽  
Recommender System ◽  
Recommendation System ◽  
Foreign Languages ◽  
Multimedia Content ◽  
Functional Requirements ◽  
Algorithmic Support ◽  
User Ratings ◽  
Selection Of

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.

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