Table 1 Summarizes previous approaches to movie recommendation and the challenge of cold start users
From: A hybrid recommender system based-on link prediction for movie baskets analysis
Authors | Ref | Methods | Advantages | Disadvantages |
|---|---|---|---|---|
Kim et al. | [17] | Collaborative error-reflected models for cold-start recommendation system | 1. High speed | 1. Low accuracy 2. Low precision |
Bobadilla et al. | [18] | A collaborative filtering approach to mitigate the new user cold start problem | 1. Normal accuracy | 1. Complex model |
Byström | [19] | Movie recommendations from user ratings | 1. Good accuracy | 1.Complex model |
Lika et al. | [20] | Facing the cold start problem in recommender system | 1. Fast execution time | 1. High MAE 2. High RMSE |
Pereira and Hruschka. | [21] | Simultaneous co-clustering and learning to address the cold start problem in recommendation system | 1. High speed | 1. Low accuracy 2. Low precision |
Sperlì et al. | [22] | A social media recommendation system | 1. Normal accuracy | 1. Complex model |
Kutty et al. | [23] | A people-to-people recommendation system using tensor space models | 1. High speed | 1. Low accuracy 2. Low precision |
Lin and Chi | [24] | Novel movie recommendation system based on collaborative filtering and neural networks | 1. Fast execution time | 1. High MAE 2. High RMSE |
Walek and Fojtik | [25] | Module combining a collaborative filtering system, a content-based system, and a fuzzy expert system | 1. High speed | 1. Complex model |