|
| Author(s) | Year | Research objective | Research method |
|
| Sun et al. [6] | 2017 | User-centric content recommendation to users of social network | Clustering based on the common interests and communication analysis |
| Yadav et al. [7] | 2021 | Movie recommendation | PCA (for feature reduction) and K-means clustering (for finding similar users) |
| Arojo et al. [8] | 2019 | Modelling the behavior of social media users based on dynamic recommender structure | Two successive classifiers for detecting similar users and their favorite contents |
| Doonan et al. [9] | 2019 | Clustering-based dynamic recommender structure | A new similarity criterion for clustering users |
| Dakotor [10] | 2015 | Presenting a new similarity criterion for selecting similar users with the target | Calculating similarity based on frequency and duration of user presence on a content |
| Gorji et al. [11] | 2020 | Hybrid recommender structure for websites | Application-based and content-based analysis of user data |
| Gunawardana and Shani [12] | 2015 | Hybrid recommender structure for websites | Distributed learning automata (for structure analysis) and Markov model (for recommendation) |
| Kung et al. [13] | 2019 | Hybrid recommender system for social networks | Modelling users motion among contents and behavior analysis |
| Latahabai et al. [14] | 2017 | Application-based dynamic recommender for social networks | Automated rules and indexing to create automated profiles for users |
| Parish et al. [15] | 2018 | Content-based recommender for social networks | Analyzing contents and generating profiles based on user behavior |
| Toth and Lengyel [16] | 2019 | Improving personalized results of search engines | Generating and continuous updating user profiles based on behavioral history |
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