SHMFSchleswig-Holstein Musik Festival (Germany)
SHMFSouthern Humanities Media Fund (Charlottesville, VA)
SHMFSouth Holland Metal Finishing Co., Inc. (Illinois)
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References in periodicals archive ?
Finally, [R.sub.1], [R.sub.2], [F.sub.1], and [F.sub.2] are input to the SHMF model to generate the prediction result.
SHMF integrates user's history behavior, user's social trust relationship, and the impact of the information of user's social hub.
In this section, effectiveness and efficiency of our SHMF model are evaluated.
Next, we employ the variable-controlling approach to adjust the parameters of SHMF model and the other three models.
We set up three experiments, PMF [2], SocialMF [4], and TS-PMF [7], as the contrastive experiments because these three methods are very often used to predict users' interests, and the three methods are in the same theoretical system as the model SHMF proposed in this paper.
It can be seen from Table 1 that the algorithm SHMF proposed in this paper improves the average accuracy by over 1.3% compared to algorithm PMF and algorithm SocialMF and the average accuracy of the algorithm SHMF is 0.76% higher than the algorithm TS-PMF.
It is found from Table 2 that the running time of the algorithm SHMF is the longest, which is nearly three times the running time of the algorithm PMF.
In the last experiment, the algorithm SHMF proposed in this paper will improve the average accuracy rate of nearly one percentage point, indicating that the user's social hub information does affect the user's interest in microblog and verifying the effectiveness of the algorithm at the same time.
Based on the work of the prediction of microblog users' interest, this paper analyzes the information of microblog users' social hub and puts forward the SHMF model, which greatly improves the top-n accuracy and average accuracy.
Caption: Figure 8: Impact of different values of different parameters in the SHMF model on performance of user interest prediction.