Evaluation of Product Usability using Improved FP-Growth Frequent Itemset Algorithm and DSLC – FOA Algorithm for Alleviating Feature Fatigue

Abstract

The development of E-commerce websites in recent years has attracted more people to buy products and services online. The development has also hooked the organizations to involve in online business processing for better growth. Generally, customers are intended to buy products with more features. Based on this traditional customer purchase behavior, the manufacturers design their product with the maximum number of features. Later, customers after using the products may get dissatisfied due to product features that are not suitable for the product. As this customer dissatisfaction is because of unwanted product features, it is termed as Feature Fatigue of a product. Nowadays customers post their opinions on E-commerce websites as reviews. For the organizations, it is the most important aspect to consider online reviews posted by existing customers. Hence those reviews may also reflect Feature Fatigue (FF) which affects the reputation and organization’s growth. To solve this feature fatigue problem a novel method linear Diminishing Step and Logistic Chaos with Fruit fly Optimization Algorithm (DSLC–FOA) based Association Rule Mining (DFARM) is proposed in this paper to evaluate the product usability. In DFARM, improved Frequent Pattern–Growth (FP-Growth) Frequent Itemset algorithm has been improvised and incorporated using DSLC-FOA algorithm to evaluate the product usability. Further feature fatigue analysis is applied using Genetic Algorithm to obtain the FF Degree through usability evaluation and capability evaluation of each feature