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%.

协作过滤是最广泛使用的推荐系统方法之一。协作过滤中的一个问题是如何使用相似性算法来提高推荐系统的准确性。最近，已经提出了一种结合了用户评价值和用户行为值的相似性算法。在评估体裁数据时，从用户得分概率中获得用户行为值。该算法的问题在于，它仅考虑体裁数据来捕获用户行为值。因此，本研究提出了一种新的相似性算法，即所谓的基于用户个人资料相关性的相似性（UPCSim），该算法检查体裁数据和用户个人资料数据，即年龄，性别，职业和位置。所有的用户资料数据都用于查找用户评价值和用户行为值相似度的权重。通过计算用户配置文件数据与用户评级或行为值之间的相关系数，可以获得两个相似度的权重。实验表明，UPCSim算法在推荐准确度方面优于以前的算法，MAE降低了1.64％，RMSE降低了1.4％。