New Top-k algorithms for Multicriteria Recommendation Systems based on Artificial intelligence and Big Data

Abstract

In the era of Big Data, parallel Top-k query processing in Information Retrieval (IR) has received growing attention for both the industry and academia. This query handling allows users to retrieve only one of the most moving data objects in a minimum set of choices. Nevertheless, most existing studies that discuss the Top_k recommendation issue focus on a single processor in a usually centralized environment, which limits the scalability of the system, adding that this problem is compounded by the use of Top-k in cases of multiple dimensions and Big Data analytics.