Recommendation Systems Based on Association Rule Mining for a Target Object by Evolutionary Algorithms

Abstract

Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done using associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and cannot be used in the real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then the Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality. 1-IntroductionOnline business success highly relies on the ability to present personal goods, services, and information items to the potential customers. This result in willigness toward recommender systems. Through statistical methods and knoweldge discovery, these systems present services to the customers [1, 2]. Collaborating filtering system is one recommender system presents recommendation through detecting similar users based on enter date and previous transactions [3]. Collaborating filtering based recommender systems have many challenges such as recommend generation speed, database sparsity, scalability, recommends utility and so on. There have been great attempts for overcoming the collaborating filtering problems and these Attempts resulted in the high quality recommender generation. The present study reviews the previous studies in this area and examines the steps and resulted findings.Recommender systems define the information systems able to analyze the previous behaviors and present the suggestions for the current issues. In other word, recommender systems try to guess the user`s thinking through his similar behavior or other similar users in order to get the best case most appropriately to the user`s taste [1, 2].There are many types of recommender systems such as:• Content-Based [4]: the working method of content filtering, based on item content analysis and trying to understand the discipline among them for generating the recommendation.

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

“…In ASARM approach, the system updates the minimum support threshold and generates new rules by traditional ARM algorithm. The authors in [37] discuss how to optimize ARM by using evolutionary algorithms and outlines the limitations of those algorithms. In [75], ASARM is applied on movie data and the results are compared with the other three collaborative recommendation techniques (correlation-based method, neural network paired with information gain and neural network paired with SVD) on similar data on movie.…”

“…In general, several approaches, have been used to develop RS such as: Collaborative Filtering (CF) [10,[13][14][15][16][17][18], content-based [1,19,20], knowledge engineering [21][22][23][24][25][26][27][28], hybrid approaches [27,[29][30][31][32][33][34][35], Associative Rule Mining (ARM) [36][37][38], fuzzy logic-based [39][40][41][42][43][44][45], Machine Learning (ML) approaches [46,47] and Conversational RS (CRS) [26,[48][49][50][51][52][53]. However, not much research has been conducted on systematic reviews of recommendation techniques applied to course RS.…”

Reliable S-Box Hardware Implementation by Gate-Level Fault Masking Enhancement

“…Evolutionary algorithms are also very popular for finding relative best rules at runtime very fast. In Varzaneh et al (2018), Association Rule Mining for a Target Object is done using PSO algorithm to reach higher speed and quality.…”

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

“…Further, researchers are investigating the use of association rule mining for collaborative filtering. Lin et al, developed an efficient Adaptive-Support Association Rule Mining (ASARM) for recommender systems [12,13]. This algorithm took advantage that the mining task is specific by fixing the head of the rule for the target user or item.…”