Abstract
Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs. This article analyses the product rank relevance provided by different commercial Big Data recommender systems (Grouplens film, Trip Advisor and Amazon); it also proposes an Intelligent Recommender System (IRS) based on the Random Neural Network; IRS acts as an interface between the customer and the different Recommender Systems that iteratively adapts to the perceived user relevance. In addition, a relevance metric that combines both relevance and rank is presented; this metric is used to validate and compare the performance of the proposed algorithm. On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer.
Highlights
Recommender systems were developed to address the search needs and relevant product selection within the Big Data from the biggest market place: the Internet
An iterative recommendation process is presented that acquires and adjusts to the perceived custoAmneritreerlaetvivaencrecaocmcomrdeinndgatoiotnheprdoicmesesnsisiopnrseosernattetrdibtuhtaetsatchqeuciruesstoamnderahdajus sintsittioalltyheseplecrctedivoedn ictussrteoqmuerstr.elevance according to the dimensions or attributes the customer has initially selected on its request
A novel approach to recommendation systems in the Big Data is proposed where the customer recursively trains the neural network while searching for relevant products
Summary
Online market places enable the trade of products provided by third party sellers while transactions are processed by the online market place operator; customers are provided with a service that search for products by their description or different properties such as department, brand, reviews or price Another relevant application of Recommender Systems is travel industry: real time travel industry’s information and services have been enabled by The Internet; customers directly purchase flight tickets, hotels and holiday packages via Web Pages and mobile applications where additional distribution costs have been eliminated due a shorter value chain.
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