Computer Engineering, Artificial Intelligence, Computer Science, Information Systems
2
Scopus Publications
Scopus Publications
Optimization of fuzzy similarity by genetic algorithm in user-based collaborative filtering recommender systems Farimah Houshmand‐Nanehkaran, Seyed Mohammadreza Lajevardi, Mahmoud Mahlouji‐Bidgholi Expert Systems, 2022 The most important subjects in the memory‐based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy‐genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold‐start challenge.
Nearest neighbors algorithm and genetic-based collaborative filtering Farimah Houshmand Nanehkaran, Seyed Mohammadreza Lajevardi, Mahmoud Mahlouji Bidgholi Concurrency and Computation Practice and Experience, 2022 Conventional recommender systems often utilize similarity formulas to identify similarities between active users and others to predict the rating of the unseen items. Existing optimization algorithms seek to find the weights and coefficients affecting these similarities. Our proposed method, implemented in R in the GACFF package, shifts away from this view and directly uses the continuous genetic algorithm to find optimal similarities in big data (e.g., Movielens 1M and Netflix datasets) to improve the performance of user‐based collaborative filtering recommendation systems. First, by identifying the users who are the nearest neighbors along with their number, the number of genes in a chromosome is determined. Each gene represents the similarity between a neighboring user and an active user. This genetic algorithm is independent of the size of the data. Our method provides optimal solutions more quickly by estimating the starting points. Moreover, the genetic metric provides better results and recommendations than previous ones in terms of runtime and quality measures (i.e., mean absolute error, coverage, precision, and recall).
