Abstract
Rating prediction is an important technology in the personalized recommendation field. Prediction results are influenced by many factors, such as time, and their accuracy directly affects the quality of the recommendation. Current time-based collaborative filtering (CF) algorithms have improved the technology of prediction accuracy to a certain extent, but they fail to differentiate the time-sensitivity of different users, which further affects prediction accuracy. To address this issue, we have proposed a rating prediction algorithm based on user time-sensitivity differences. First, we analyzed and modeled the time sensitivities of users, utilized cosine distance and relative entropy to build a judgment function, and then judged the time sensitivities of users based on a voting strategy. Next, we applied the time-sensitivity difference to improve the traditional CF algorithm and optimized the combination of parameters. Finally, we tested our algorithm on standard datasets. The experimental results showed that there are many users who have different sensitivities to time. According to these experimental results, our proposed algorithm has achieved a higher prediction accuracy than other state-of-the-art algorithms.
Highlights
With the penetration of information technology into every aspect of people’s personal lives and work, users are disseminators of information and producers of it
Information 2020, 11, 4 preferences are not taken into account when making rating predictions of new movies for user A, the weights of these two movies in the process of item-based collaborative filtering (CF) recommendation would be the same, which fails to account for the fact that user A currently has a higher preference for sci-fi movies
In this study, we applied the time-sensitivity difference to improve the traditional CF algorithm and proposed a rating prediction algorithm based on user time-sensitivity
Summary
With the penetration of information technology into every aspect of people’s personal lives and work, users are disseminators of information and producers of it. Information 2020, 11, 4 preferences are not taken into account when making rating predictions of new movies for user A, the weights of these two movies in the process of item-based CF recommendation would be the same, which fails to account for the fact that user A currently has a higher preference for sci-fi movies. The quality of the recommendation will be low For this reason, researchers have proposed many time-weighted CF algorithms, some of which achieved better results than the traditional CF algorithm [7]. Researchers have proposed many time-weighted CF algorithms, some of which achieved better results than the traditional CF algorithm [7] They did not consider that different users would have different degrees of sensitivity to time. In this study, we applied the time-sensitivity difference to improve the traditional CF algorithm and proposed a rating prediction algorithm based on user time-sensitivity
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