And, we proposed the cooperative filtering recommendation system based on the dynamic similarity of different users.
In order to improve the prediction accuracy of cooperative filtering algorithm, the user's target content would be processed with crowd scheme.
In cloud compressed video frames, the cooperative filtering of cloud equipment can denoise the multimedia stream, which could obtain more cloudy compression gain.
In order to improve the prediction accuracy of cooperative filtering algorithm, the user's target content is processed with crowd scheme, and then, the system is fused with the recommendation system.
To solve a large number of Internet garbage information and meaningless data reduce the user's Internet experience, according to the changing needs of users, the cooperative embedded filter crowd content recommendation mechanism was proposed to enhance the efficiency and accuracy of use information from the large amount of network resources.
To implement the cooperative ZF filter at transmitter-side, the transmitters are required to exchange the received feedback signal and the CSI of the reverse channels G=[g 1,...,g M ], where g i is the channel from the ith receiver to all transmitters, which incurs relatively small overhead of channel exchange due to the high-speed intra-cluster links.
To achieve the V-MIMO feedback scheme as illustrated in Fig. 1, each of M receivers simultaneously transmits their estimated channel vectors {h 1,...,h M } to the transmitters, where the M feedback streams are jointly detected by the cooperative ZF filter over whole feedback channel.
Figure 3 shows the recommendation accuracy with transparent fusion comparison results of the proposed fusion mechanism of cooperative embedded filtering and crowd content recommendation (FCECR) algorithm and recommendation algorithm based on ant colony (RAAC).
The experimental results show that the fusion mechanism of cooperative embedded filtering and crowd content recommendation has obvious advantages in terms of content recommendation accuracy, reliability, and convergence speed.
For the accuracy performance of belief propagation with particle filters over Kalman filters and cooperative least square algorithms has already been proved by existing work such as[28, 29], we focused on evaluating the robustness of BOPU.
