Open AccessAn enhanced framework for solving cold start problem in movie recommendation systems
Author(s) -
Salma Adel Elzeheiry,
N. E. Mekky,
Ahmed Atwan,
Noha A. Hikal
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i3.pp1628-1637
Subject(s) - movielens , mean squared error , computer science , rss , feature (linguistics) , recommender system , similarity (geometry) , logistic regression , product (mathematics) , artificial intelligence , mean absolute error , machine learning , data mining , statistics , mathematics , collaborative filtering , world wide web , linguistics , philosophy , geometry , image (mathematics)
Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N and precision@N and it is applied to 1M, 10M and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791 and 0.8739.