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

Hossein Hatami Varzaneh,Behzad Soleimani Neysiani,Hassan Ziafat,Nasim Soltani

doi:10.28991/esj-2018-01133

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.

Highlights

Online business success highly relies on the ability to present personal goods, services, and information items to the potential customers
The present study examined the recommender systems and among existing methods, the introduced algorithms were compared focusing on collaborating filtering based on association rules mining
The evolutionary algorithms and the concept of multi-objective optimization functions are put in this algorithm and MOPSO-Association Rule Mining (ARM) and ARM were analyzed by genetic algorithm

Summary

1- Introduction

Online business success highly relies on the ability to present personal goods, services, and information items to the potential customers. That is why Adaptive-Support Association Rule Mining (ASARM) was presented to effect rulesquality by the least support adjustment This not an appropriate algorithm because it performs the Apriori algorithms several times during the operation steps and output with low efficiency. Considering the fact that support and the confidence threshold values effect on the quality of Apriori algorithm output rules, the Adaptive-Support Association Rule Mining (ASARM) has been proposed [12, 15] In this algorithm, rule generation has been done by a CBA-RG algorithm which is the evolutionary version of the Apriori algorithm. The basic concepts of natural evolution are inspired in an abstract way to look for finding an optimized solution for different problems There are methods such as quick search, binary search, first deep, and first surface search that return the responses in a certain way. For problems with discrete variables having several optimized responses the traditional methods could not be applied

There are three solutions for multi-objective problems as the following:

4- Conclusion

Findings

12- References