Abstract
In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.
Highlights
Recommender systems are software gears and techniques which offer recommendations for users
To address the challenges of more effective recommendations generation, we have proposed a content-based recommender system and context-aware recommender system
Context-aware recommender systems have been swiftly blooming as an effective way for relevant and useful learning retrieval according to contextual information
Summary
Recommender systems are software gears and techniques which offer recommendations for users. The recommendation system provides recommendations by keeping user interest and using contextual information into account. Still, there is a great need to resolve issues like abundant information, data redundancy, and context redundancy to generate more effective recommendations [5]. To address the challenges of more effective recommendations generation, we have proposed a content-based recommender system and context-aware recommender system. Context-Aware Recommender Systems (CARS) is a specific category of recommender systems that takes contextual information as an input and provides additional useful suggestions. Recommender frameworks provide customized counsel to clients about things they may be keen on These apparatuses help individuals proficiently oversee content overburden and lessen multifaceted nature while hunting down essential data. We have discussed context-aware recommender systems that will provide us with the idea that contextual information can provide better recommendations. We have taken knowledge-based resources into consideration which are generated by ontology-based recommenders
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