Copyright © 2024 by Author/s and Licensed by Kuey. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Educational Administration: Theory and Practice 2024,30(4), 1239-1250 ISSN:2148-2403 https://kuey.net/ Research Article Recommendation Systemfor Daily Consumer Purchases List Usingspecial Hybrid Approach Sanaa Al Rawahi 1 , Nasser Al Musalhi 2* , Erbug Celebi 3 1 Department of Information Technologies, Cyprus International University, Nicosia, North Cyprus, TURKEY Mersin 99258 2* Department of Information Technology, University of Technology and Applied Sciences (Ibra-Branch), Ibra, Sultanate of Oman. 3 Artificial Intelligence Application and Research Center, Cyprus International University, Nicosia, North Cyprus, TURKEY Mersin 99258 *Corresponding Author: Nasser Al Musalhi Email: nhsoman2015@gmail.com Citation: Nasser Al Musalhi et al. (2024), Recommendation Systemfor Daily Consumer Purchases List Usingspecial Hybrid Approach, Educational Administration: Theory and Practice, 30(4), 1239-1250, Doi: 10.53555/kuey.v30i4.1642 ARTICLE INFO ABSTRACT Recommendation systems have attained widespread prevalence in the current digital world, providing consumers with specific recommendations for a diverse range of products, services, and information. These systems have a significant role in shaping customer behavior, particularly in the realm of online shopping. This study aims to evaluate the performance of a hybrid recommendation system in suggesting daily purchase items to consumers by examining three different recommendation system methods: collaborative filtering (CF), Content-based filtering (C-B), and Hybrid. Then we assess their efficiency in delivering suggested items to consumers through the utilization of recall, precision, and F1 metrics. The study reveals that each strategy exhibited distinct strengths. However, the hybrid approach is considered the most effective method for recommending items for new users who do not have a history profile. 1. Introduction System recommendations refer to software tools and methodologies that offer suggestions for products and services that could potentially capture the user's interest. These activities encompass a variety of decision- making processes, including the selection of things for purchase, the consumption of music, and the perusal of online news articles. The recommendations have been categorized into three distinct groups. When users are provided with suggestions by the system, these suggestions are commonly referred to as "items" in a general sense. Recommender Systems often exhibit specialization towards a specific category of items, such as music or news. Consequently, the system's design, graphical user interface, and core recommendation technique are customized to offer valuable and efficient suggestions for the given item category[1].The understanding of referral systems, also known as recommender systems or recommendation engines, might provide difficulties in terms of comprehension. All these systems serve a comparable purpose, as they leverage customers' historical behavior and current preferences to anticipate their future preferences. Gaining insights into consumer expectations can be achieved by examining their purchase behaviors. Understanding the factors that influence consumer purchasing behavior is of utmost significance. Prior to its release onto the market, a comprehensive evaluation of a product is important to ascertain its acceptance among consumers. Marketers could get knowledge regarding the preferences and aversions of their intended audience, which can then be utilized to strategize and execute their marketing initiatives. Research on consumer purchasing behavior, encompassing the analysis of individuals' product preferences, the underlying motivations driving their purchases, the timing of their buying decisions, the frequency of their purchases, and related factors, is widely prevalent in academic literature. [2]. A recommendation system is a crucial component of many modern platforms, designed to predict and suggest items that users might be interested in, based on their preferences and behaviors. These systems leverage various algorithms and techniques to analyze large amounts of data, typically including user interactions, item attributes, and contextual information. There are three primary types of recommendation systems: content-based, collaborative filtering, and hybrid systems. This study assesses the efficacy of three recommendation system approaches in providing clients with suggestions for daily consumable products. By using historical data, these systems may optimize customer buying patterns by suggesting the most relevant products for daily needs. Moreover, the research examines the use of recommendation algorithms to provide customized shopping rosters by leveraging consumer