  Information technology has spread widely, and extraordinarily large amounts
of data have been made accessible to users, which has made it challenging to
select data that are in accordance with user needs. For the resolution of the
above issue, recommender systems have emerged, which much help users go through
the process of decision-making and selecting relevant data. A recommender
system predicts users behavior to be capable of detecting their interests and
needs, and it often uses the classification technique for this purpose. It may
not be sufficiently accurate to employ individual classification, where not all
cases can be examined, which makes the method inappropriate to specific
problems. In this research, group classification and the ensemble learning
technique were used for increasing prediction accuracy in recommender systems.
Another issue that is raised here concerns user analysis. Given the large size
of the data and a large number of users, the process of user needs analysis and
prediction (using a graph in most cases, representing the relations between
users and their selected items) is complicated and cumbersome in recommender
systems. Graph embedding was also proposed for resolution of this issue, where
all or part of user behavior can be simulated through the generation of several
vectors, resolving the problem of user behavior analysis to a large extent
while maintaining high efficiency. In this research, individuals most similar
to the target user were classified using ensemble learning, fuzzy rules, and
the decision tree, and relevant recommendations were then made to each user
with a heterogeneous knowledge graph and embedding vectors. This study was
performed on the MovieLens datasets, and the obtained results indicated the
high efficiency of the presented method.
