Skip to main content

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