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.

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).

Extracting of association rules between urban features provides latent and considerable information for urban planners about the relationships between urban characteristics and their similarities. For this purpose, in this paper, the most famous and well-known Apriori algorithm is used. We present the Fariori algorithm to delay the characteristics that can be deleted during execution, as well as to achieve main and frequent features in the early stages with efficient changes to the Apriori algorithm. Although the spatial and temporal complexity of both algorithms is exponential based on the number of fea-tures, in practice, by implementing the Fariori algorithm in MATLAB, we achieved more rules than the existing software (R, Weka, Market Basket Analysis and, Yarpiz). In the proposed algorithm, it is possible to determine the degree of similarity by adjust-ing the support and confidence ratio parameters to identify a coherent set of similar cities. The used database includes cities of 31 in the provincial capitals of Iran. Dis-covering the association rules leads to similar cities finding and can be an efficient aid in the decision-making process.