The Influence of Bandit-Based User Openness Feature on Recommendation Diversity and Accuracy

Collaborative filtering algorithms have obvious advantages in recommendation accuracy, and Bandit's algorithm is a strategy to address diversity needs. The COFIBA algorithm combines the collaborative filtering algorithm with the Bandit algorithm to provide a solution for recommending the balance of diversity and accuracy. However, COFIBA does not consider the influence of time characteristics, and COFIBA is a cumulative regret. It is relatively slow to solve the problem of diversity. Therefore, this paper proposes a learning-based model. On the one hand, it introduces the openness characteristics of users to achieve diversity recommendation, and relies on the "exploration-feedback-update" strategy to adjust the user's openness. At the same time, the time factor is incorporated into the COFIBA algorithm as a feature, and the change of user interest with time is analyzed to ensure the accuracy of recommendation. The experimental results show that the combination algorithm with time and open features has a significant improvement in the diversity and accuracy of the results compared with the COFIBA algorithm.