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

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.

Intelligent Recommendations of Startup Projects in Smart Cities and Smart Health Using Social Media Mining

“…We evaluated and compared our approach RecSPSC with the widely deployed recommendation approaches, Collaborative Filtering (CF) [44,45] and Content-Based (CB) [46]. e comparison is performed according to the precision, recall, and F-measure averages (see Table 8 and Figure 7).…”

“…ere are commonly known recommendation algorithms that can be applied or adapted to different fields [42,43]. e most recommender approaches are as follows: based on Collaborative Filtering (CF) [44,45] and Content-Based (CB) [46]. e prosperity of a business and the investment returns depend on the startup's choice.…”

Columns Occurrences Graph to Improve Column Prediction in Deep Learning Nlidb

“…Our experiments show that our approach improves exact match accuracy. We have compared our results with a base model of SyntaxSQLNet and two other contributions that have implemented preprocessing on input feature vectors and integrated with SyntaxSQLNet [14][15][16]. We show that our work improved accuracy up to 10% in an experiment with BERT embedding.…”

E-Learning Recommendation System for Big Data Based on Cloud Computing

“…Recommender Systems (RSs) are automated tools and strategies that allow recommendations to users about items that may be of importance to them. For Schein et al [10] Recommender systems indicate users' items of interest depending on their direct and indirect interests, other users' interests, and user and item features. Any of the practical implementations that use such devices may involve (recommending books, products, videos, jobs, music).…”