Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

Abstract

Recommender systems that possess adequate information about users and analyze their information, are capable of offering appropriate items to customers. Collaborative filtering method is one of the popular recommender system approaches that produces the best suggestions by identifying similar users or items based on their previous transactions. The low accuracy of suggestions is one of the major concerns in the collaborative filtering method. Several methods have been introduced to enhance the accuracy of this method through the discovering association rules and using evolutionary algorithms such as particle swarm optimization. However, their runtime performance does not satisfy this need, thus this article proposes an efficient method of producing cred associations rules with higher performances based on a genetic algorithm. Evaluations were performed on the data set of MovieLens. The parameters of the assessment are: run time, the average of quality rules, recall, precision, accuracy and F1-measurement. The experimental evaluation of a system based on our algorithm outperforms show than the performance of the multiobjective particle swarm optimization association rule mining algorithm, finally runtime has dropped by around 10%.

Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation

“…Considering the structural relationship between users, a modeling method for time-series behavior among users is proposed [26]. Some scholars seek similar users by effectively analyzing the trust relationship between users [27]. Some other scholars use relevant theories and methods to cluster items and predict unrated items in the same category to densify the scoring matrix in order to mitigate the adverse impact of scoring sparsity on the collaborative filtering algorithm [28].…”

Solving Image Processing Critical Problems Using Machine Learning

“…They are a subset of evolutionary algorithms. Genetic algorithms take an iterative approach to problem-solving, generally following these steps [63,64].…”

“…In DCT for optimal compression genetic algorithm could be a better solution for the optimization to improve the quantization with in the image.GA also applied in the fractional image compression. In fractal compression are searching the domain blocks and transforming the domain block to match the range blocks, it is these processes that are optimized by the genetic algorithm from Mtira and Morthy (1998) [64].…”

“…Figure shows Lena, uncompressed on the left, and compressed with the neural network on the right. The loss of information is particularly noticeable around the hair and the band on the hat, due to this being high-frequency areas [64].…”

Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems

“…Other recent efforts leveraging argumentation-based tools are [8,9]. Finally, other relevant work involves applying data-driven approaches, as we will discuss below [10,11]. None of these efforts focus on software development methodologies-this work thus aims to present a set of guidelines to support knowledge and software engineers in the analysis and design of argumentation-based recommender systems (ABRS), aiming to fill this gap in the current intelligent systems development literature.…”