  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.
