Search

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the interest of solving the review honesty problems in current recommendation algorithms based on review analysis, and the data sparsity problems in traditional collaborative filtering algorithm, this paper involved the modified user preferences and user trust into traditional collaborative filtering algorithm, and proposed a new user-based collaborative filtering algorithm based on user ratings, reviews and user trust by analyzing the topic distributions and feedback information included in reviews from users. In detail, with the consideration of users’ reviews, it investigated the distribution of item features on different topics, and the level of preference of different users, to generate topic distribution of users’ reviews. To continue, this paper calculated the value of review diversity to magnify distinctive features in topic distribution, and generated the value of review helpful-degree by utilizing review feedback data to rectify user preferences and reduce the influences from spam reviews. On this basis, it further introduced user trust to enhance the accuracy of user similarity. The results of experiment on real dataset show that the proposed algorithm can improve the quality of prediction performance. [ABSTRACT FROM AUTHOR]

In order to overcome the sparsity problem of traditional collaborative filtering algorithm, combining the characteristics of user ratings with user reviews, litis paper proposed user-based collaborative filtering with reviews and ratings, and item-based collaborative filtering with reviews and ratings. The algorithms utilized topic model to generate review topics distribution, and utilized ratings to generate review altitude. Then it used the review altitude to magnify the prominent aspects of review topics distribution, and finally predicted ratings and made recommendations. The experimental results show that the proposed algorithms can improve the quality of recommendation in sparse datasets. [ABSTRACT FROM AUTHOR]

Due to the envelope filling problem exists in the process of empirical mode decomposition,this paper proposed an improved algorithm which could eliminate the undershoot phenomenon exactly. By introducing pseudo-extreme points,this algorithm increased ihe number of extreme points and formed new extreme value sequence. Then it got new envelope by using the new extreme value sequence interpolation. The envelope fitted by this method was closer to the original signal and had better smoothness. Finally,a contrast result between cubic spline interpolation and this algorithm show that the number of undershoots decreases by about 77.5% ,which proves that this algorithm can effectively reduce ihe number of undershoot points. In addition, filling the envelope can lighlly wrap the original signal and have a better envelope. [ABSTRACT FROM AUTHOR]

Fingerprint orientation field estimation is a very important step in fingerprint identification. While classical methods such as the gradient based can not well cope with latent fingerprint images, a recently proposed dictionary based method has some difficulty around the singular points in fingerprints. To further improve its performance, this paper incorporated the zero-pole model into the dictionary model. It considered the fingerprint field orientation was consist of point-charge orientation and residual orientation. Then it used dictionary model to denoise the residual orientation and used the zero-pole model to generate correct point-charge orientation. At last, it reconstructed the latent fingerprint field orientation by adding back the influence of singular points to the denoised residual orientation. By adopting this method, it got better fingerprint orientation and solved the “real-word” problem. Evaluation experiments were done on the NIST SD27 database. The experiment results show that the proposed algorithm can extract more accurate orientation field from the latent fingerprint image than the dictionary model, and help to achieve higher latent fingerprint recognition accuracy. [ABSTRACT FROM AUTHOR]