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Abstract：
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. © 2019 IEEE.
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Year： 2019
Page： 1624-1628
Language： English
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WoS CC Cited Count： 0
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ESI Highly Cited Papers on the List： 0 Unfold All
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30 Days PV： 2
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