M.A.R.S: Multi-Agent Recommender System

M.A.R.S is a multi-agent recommender system with a set of autonomous agents working in a cooperative system to monitor the students's learning path and provide them with the appropriate resources for each situation to make their learning experience more effective.

M.A.R.S is developed by Youness ER-RAIBA, the system design is inspired from the research paper "Intelligent Digital Learning: Agent-Based Recommender System" by Imène Brigui-Chtioui, Ph. D, Philippe Caillou, Ph. D and Elsa Negre, Ph. D.

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MULTI-AGENT RECOMMENDER SYSTEM

M.A.R.S, THE FUTURE OF E-LEARNING

Mars Diagram
         

RECOMMENDER AGENT

The recommendation process is initiated by the recommender agent, which identifies the relevance of the events, scores the activities based on the student's profile and provides a ranking of potentially interesting resources to the relevant filtering agent.

         

FILTERING AGENT

Each filtering agent is associated with a unique course, it manages the results obtained from the recommender agent, chooses the most appropriate one, sends it to the learner and communicates this decision for information to the manager agent.

       

MANAGER AGENT

The manager agent is connected to all the other agents in the system, it centralizes all the information gathered from the decisions made by the filtering agents and the student's feedback to evaluate the relevance and quality of the recommended resources and accordingly update the recommendation system.

HOW M.A.R.S WORKS?

The recommender agent evaluates and scores the activities of each student profile. The filtering agent manages the results obtained from the recommender agent and selects the appropriate one, then sends recommendations to the student and informs the manager agent of its decision. The manager agent collects feedback from the students on the suitability of the recommended resources and on the decisions of the filtering agents. Then, based on all these information, it makes the decision to update the recommendation system.

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FEATURES OF M.A.R.S

    

REAL-TIME RECOMMENDATION

Improves the learning experience for each student

         

SENTIMENT ANALYSIS

Analyze and classify attitudes of students toward the recommendation services.

        

EASY TO CUSTOMIZE

Provides levels of personalization

            

ADAPTABILITY

Improves its recommendations based on students feedback

         

AI POWERED

Powered by state of art AI algorithms

        

PRIVACY ORIENTED

Respects students data privacy and provides security and stability

         

SCALABILITY

Evolves as student information is updated and new students enroll in courses.

DIFFERENTIATED INSTRUCTION

Each student is unique and deserves a personalized learning experience tailored to his/her preferences and abilities.

       

SMART REPORTING

Reporting is an integral part of MARS, allowing for informed decision making.

MULTI-AGENT RECOMMENDER SYSTEM

M.A.R.S

Standard

    Usage Plan Per Month


10,000+ Recommendation Requests

Up to 50 Active Students

    M.A.R.S AI


Machine Learning/AI

Personalization 

Advanced NLP

    Delivery


Cloud-Based

Data Center Failovers & Load Balancing

Dedicated Infrastructure

Dedicated with Failover

Uptime SLA   99.90%

    Platforms & Integration


Moodle, LifterLMS, LearnDash, Sensei LMS Integration

API Interface

    Support & Services


Knowledge Base

Email and Ticket Support

Access to  Group Training

Live Webinars

Contact Us

Plus

    Usage Plan Per Month


50,000+ Recommendation Requests

Up to 100 Active Students

    M.A.R.S AI


Machine Learning/AI

Personalization 

Advanced NLP

    Delivery


Cloud-Based

Data Center Failovers & Load Balancing

Dedicated Infrastructure

Dedicated with Failover

Uptime SLA   99.90%

    Platforms & Integration


Moodle, LifterLMS, LearnDash, Sensei LMS Integration

API Interface

    Support & Services


Knowledge Base

Email and Ticket Support

Access to  Group Training

Live Webinars

Contact Us
    Usage Plan Per Month


250,000+ Recommendation Requests

Up to 250 Active Students

    M.A.R.S AI


Machine Learning/AI

Personalization 

Advanced NLP

    Delivery


Cloud-Based

Data Center Failovers & Load Balancing

Dedicated Infrastructure

Dedicated with Failover

Uptime SLA   99.90%

    Platforms & Integration


Moodle, LifterLMS, LearnDash, Sensei LMS Integration

API Interface

    Support & Services


Knowledge Base

Email and Ticket Support

Access to  Group Training

Live Webinars

Contact Us

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