User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

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%.

Automated summary

A new User Profile Correlation-based Similarity (UPCSim) algorithm is proposed to improve recommendation system accuracy by considering user behavior and rating values, and using user profile data to find similarity weights. Compared to the previous algorithm, the UPCSim algorithm outperforms in recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.