Recommender Systems (RS) provide personalized recommendation according to the user need by analyzing behavior of users and gathering their information. One of the algorithms used in recommender systems is user-based Collaborative filtering (CF) method. The idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. The important part of Collaborative filtering algorithms is allocated to determine similarity between objects. Similarities between objects are classified to user-based similarity and item-based similarity. The most popular used similarity metrics in recommender systems are Pearson correlation coefficient, Spearman rank correlation, and Cosine similarity measure.Until now, little computation has been made for optimal similarity in Collaborative filtering by researchers. For this reason, in this research, we propose an optimal similarity measure via a simple linear combination of values and ratio of ratings for user-based Collaborative filtering by the use of Firefly algorithm; and we compare our experimental results with Pearson traditional similarity measure and optimal similarity measure based on genetic algorithm. Experimental results on real datasets show that proposed method not only improves recommendation accuracy significantly but also increases quality of prediction and recommendation performance.

This study introduces a tailored recommendation system aimed at enriching Iran’s tourism sector. Using a hybrid model that combines neural Collaborative filtering (NCF) with matrix factorization (MF), our approach leverages both demographic and contextual data of combined tourist-landmark (4177 samples) to provide personalized touristic recommendations. Empirical evaluations on the implemented methods show that the hybrid model outperforms factorization techniques, achieving a test F1 score of 0.84, accuracy of 0.90, and a test error reduction from 0.83 to 0.37. Feature vector integration further improved test recall by 17%, underscoring the model's robustness in capturing user-item relationships. Further analysis using t-SNE as well as visual analyses of embedding structures confirm the systems ability to generalize patterns in latent space; thereby, mitigating cold-start problem for new tourists or unvisited landmarks. This study also contributes a structured dataset of Iranian landmarks, user ratings, and supplementary contextual data for fostering future research in culturally specific intelligent recommender systems. For implementation details, refer to the GitHub repository at https://github.com/MsainZn/Collaborative_filtering_Tourism_Landmarks.

The growth amount of information on the Web makes it difficult for many web users to make decision and choose either information or goods. Thus, a recommender system is an approach that helps users to obtain their needs according to her/his preference within a massive amount of information rapidly without waste of time. The main advantage of using a recommender system in any online shopping or social media like Amazon, Net ix and Facebook is to increase the percentage of overall pro ts, customer satisfaction and retention. In this paper, we introduce an approach to increase the accuracy and to improve the performance of Collaborative  ltering recommender system. In this paper a hybrid approach is proposed to improve the performance of video Collaborative  ltering recommender system based on clustering and evolutionary algorithm. Proposed approach combines k-means clustering algorithm and two different evolutionary algorithms which are Accelerated Particle Swarm Optimization Algorithm (APSO) and Forest Optimization Algorithm (FOA). The main aim of this paper is to increase the accuracy of recommendation of user-based Collaborative  ltering video recommender system. Evaluation and computational results on the MovieLens dataset show that the proposed method has a better performance than the other related methods.