An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior

Abstract

Abstract. With the sharp increment of information on the Internet, many technologies have been proposed to solve the problem of information explosion in people's life. Collaborative Filtering (CF) recommendation system is one of the most popular and efficient ways of solutions, especially item based CF systems. While traditional item based CF recommendation algorithms either ignore the diversity of different users' rating behavior or do not deal with it efficiently. In this paper, we present a novel similarity function using the average rating for each user instead of the overall average rating for all users. In order to find the optimal similarity function, we use genetic algorithm (GA) to optimize the weight vectors associated to the similarity function. A series of comparison experiments are conducted to demonstrate the effectiveness in terms of the quality of prediction of the proposed method.

A Genetic-Based Pairwise trip Planner Recommender System

“…Another recent study by Gasmi et al in 2021 [21] also proposed a user-based collaborative filtering combined with the GA based meta-heuristic. A study by Xiao [22] proposed a combination of item-based collaborative filtering with GA which is called itemCFGA. This study also proposed a novel similarity function which uses the average rating of each users.…”

Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

“…e potential relationship between information mining users provides new ideas for finding neighbors. Researchers have also used demographic knowledge [13,14] to achieve major breakthroughs, while some scholars used score ranking prediction methods to enhance recommendation performance such as [15][16][17], and others chose the genetic algorithm used in the prediction process to improve recommendation performance, such as [18,19]. Context as a dynamic description of an item and a user's situation affects the user's decision-making process; hence, it is essential for any recommendation system in a big data environment [20][21][22].…”