Abstract
Collaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.
Highlights
The exponential growth of information on the internet causes users can get huge information resources to dig up and collect
A similarity algorithm was recently proposed in [31] that combines similarity based on user rating value and similarity based on user behavior value, which requires genre data
The contribution of our study consists of two things: 1. The proposed User Profile Correlation-based Similarity (UPCSim) algorithm utilizes all user behavior data provided in the MovieLens 100K dataset to calculate the weights of similarity based on user rating value and similarity based on user behavior value
Summary
The exponential growth of information on the internet causes users can get huge information resources to dig up and collect. This flood of information causes users difficulty accessing the desired information [1, 2]. One of the tools to solve this problem in analyzing user interests is a recommendation system. The recommendation system’s main task is to offer users personalized item recommendations through information filtering. This system has become a commercial platform that recommends users to select the desired items. The recommended items are useful to support users in various decision-making processes, such as what books to read, which locations to visit, what news to read, and more [7]
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