Abstract
The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density.
Highlights
In the era of information explosion, it is difficult for us to select useful information before we make a decision among a large number of choices in a short time
By proposing a new user-based collaborative filtering algorithm termed Asymmetric User Influence and Global Importance Values (AUI&GIV), we aim to address the above issues by designing an asymmetric user influence model and utilizing the PageRank algorithm to calculate the global importance values
The accuracies generated by the Slope One algorithm are quite poor on the four datasets, i.e., its Mean Absolute Error (MAE) or Root Mean Square Error (RMSE) is quite large and the AUI&GIV algorithm can achieve the improvement from 9% to 17%
Summary
In the era of information explosion, it is difficult for us to select useful information before we make a decision among a large number of choices in a short time. The goal of the recommendation system is to filter useful information from an enormous amount of information so that we can predict the rating that a user would give to an item and recommend items to the right users. Recommendation algorithms can be classified into three different types [11], namely collaborative filtering algorithm [12,13,14], content-based algorithm [15] and hybrid recommendation algorithm [16].
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