Adaptive Multi-Agent E-Learning Recommender Systems
Educational recommender systems have become a necessity in the recent years due to overload of available educational resource which makes it difficult for an individual to manually hunt for the required resource on the internet. E-learning recommender systems simplify the tedious task of gathering the right web pages and web documents from the scattered world wide web repositories according to every users' requirements thus increasing the demand and hence the curiosity to study them. Retrieval of a handful of recommendations from a very huge collection of web pages using different recommendation techniques becomes a productive and time efficient process when the system functions with a set of cooperative agents. The system is also required to keep up with the changing user interests and web resources in the dynamic web environment, and hence adaptivity is an important factor in determining the efficiency of recommender systems. The paper provides an overview of such adaptive multi-agent e-learning recommender systems and the concepts employed to implement them. It precisely provides all the information required by a researcher who wants to study the state-of-the-art work on such systems thus enabling him to decide on the implementation concepts for his own system.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsRetrievalSimilar Papers 제목 키워드 기반
Incentive-Aware Recommender Systems in Two-Sided Markets
Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or "arms") to users (or "agents"). A key challenge in this domain arises from myopic agents who are naturally inc…
FairnessRecommendation SystemsVocal Bursts Valence PredictionAgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
The emergence of agentic recommender systems powered by Large Language Models (LLMs) represents a paradigm shift in personalized recommendations, leveraging LLMs' advanced reasoning and role-playing capabilities to enabl…
BenchmarkingRecommendation SystemsMulti-Agent Video Recommenders: Evolution, Patterns, and Open Challenges
Video recommender systems are among the most popular and impactful applications of AI, shaping content consumption and influencing culture for billions of users. Traditional single-model recommenders, which optimize stat…
Multi-agent Reinforcement LearningRecommendation SystemsUNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution
In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems. Since industrial recommender systems are typically designed as multi-stage…
Multi-agent Reinforcement LearningRecommendation Systemsreinforcement-learningReinforcement Learning+1Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. However, learning to train an optimal RL ag…
counterfactualData AugmentationDeep Reinforcement LearningRecommendation Systems+2