A Review of Content-Based and Context-Based Recommendation Systems

In our work, we have presented two widely used recommendation systems. We a context-aware recommender system to filter the items associated with user’s interests coupled context-based prescribe those items. this study, systems perceive location, time, and company. The retrieves patterns from World Wide Web-based on past interactions provides future news recommendations. different techniques support media recommendations for smartphones, create framework context-aware, E-learning content, deliver convenient user. To achieve goal, content-based, collaborative filtering, hybrid system, implemented Web ontology language (OWL). also Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, natural languages that suggest user related search. E-paper provide users required news. After applying reasoning approach, concluded by some means, approach works similarly as content-based since taking gain of can recommend according interests. additional options or results rely ratings, appraisals,