Users’ Rating Predictions Using Collaborating Filtering Based on Users and Items Similarity Measures

Table 2

Comparison of existing works of content-based filtering.

Year refer.	Method	Dataset	Results
2018 [24]	E-learning recommender system utilizing negative rating	5 groups of students (each have 25 students)	F 35.381
2019 [22]	Semantic web mining approach for recommender system	Web textual dataset	Increases 5.2% accuracy
2019 [23]	Traditional recommender system context by using content based and link stream features	Goodreads MovieLens 20M	RMSE 0.8095
2021 [25]	Content-based group recommendation systems (CB-GRS)	MovieLens 100K HetRec	Precision metric 0.5167