Abstract:
With the increasing amount of data content produced daily, it becomes very di?cult
for users to ?nd the resources suitable to their needs. Recommendation systems are
proposed to solve this problem and are capable of providing personalized recommendations
or guiding the user to interesting or useful resources within a large data space. Recently,
Recommender systems are getting importance due to their signi?cance in making decisions
and providing detailed information about the required product or a service. In this paper,
we conduct a systematic review for recommendation models, and discuss the challenges
and open issues. Furthermore, we propose a new recommendation system ontology-based
in which machine-learning algorithms are used to achieve user needs identi?cation and
provide precise recommendations.
