Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter

Abstract

Analyzing and processing massive volumes of data in different applications like sensor data, health care and e-Commerce require big data processing technologies. Extracting useful information from the enormous size of unstructured data is a crucial thing. As the amount of data becomes more extensive, sophisticated pre-processing techniques are required to analyze the data. In social networking sites and other online shopping sites, a massive volume of online product reviews from a large size of customers are available [1]. The impact of online product reviews affects 90% of the current e-Commerce market [2]. Customer reviews contribute the product sale to an extent and product life in the market depends on online product recommendations. Online feedback is one of the communication methods which gives direct suggestions from the customers [3, 4]. Online reviews and ratings from customers are another information source about product quality [5, 6]. Customer reviews can help to decide on a new successful product launch. Online shopping has several advantages over retail shopping. In retail shopping, the customers visit the shop and receive price information but less product

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

“…The model‐based algorithms (Khadem & Forghani, 2020; Lima et al, 2020) follow two steps: The algorithm handles several matrices to generate an efficient model for representing the original rating matrix. The generated model is then used to estimate the predictions of active user ratings. The most popular models for making recommendations include Bayesian classifiers (Gao et al, 2020), neural networks (Bandyopadhyay & Thakur, 2020), fuzzy systems (Barzanti et al, 2020), genetic algorithms (GAs) (Moses & Babu, 2020), latent features (Da'u et al, 2020), and matrix factorization (Liu & Ye, 2020). On the other hand, based on the steps outlined below, memory‐based algorithms (Mallik & Sahoo, 2020; Narayanan et al, 2020) use the entire rating matrix to achieve predictions: The prediction ratings of the active user are estimated from the ratings of their neighbours. The similarity metrics are used to measure the distance between two users or two items by their ratings. Memory‐based methods are divided into two main algorithms: User‐based algorithms, where the method for obtaining neighbours works based on the users. Item‐based algorithms, where neighbours are obtained based on the items. There are two main issues in RSs: cold start and sparsity. Each of these affects the quality of recommendations and the accuracy of the predictions.…”

“…The most popular models for making recommendations include Bayesian classifiers (Gao et al, 2020), neural networks (Bandyopadhyay & Thakur, 2020), fuzzy systems (Barzanti et al, 2020), genetic algorithms (GAs) (Moses & Babu, 2020), latent features (Da'u et al, 2020), and matrix factorization (Liu & Ye, 2020). On the other hand, based on the steps outlined below, memory-based algorithms (Mallik & Sahoo, 2020;Narayanan et al, 2020) use the entire rating matrix to achieve predictions:…”