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RECOMMENDED SYSTEM OF COLLABORATIVE FILTERING BASED IN SOCIAL NETWORKS

机译:基于社交网络的协作筛选推荐系统

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Sharing Information Among Friends Using different online Social Network such as What's Up ,Twitter Face book between two are more friends not only sharing profile update ,Audio, Videos with her .his direct friends are mutual Friends ,Online Social Network connect with globally direct are Indirect Friends Using Online social network sharing any product are comment and ranking it process Unique Challenge and opportunities for recommendation. This Experiment with real online voting trace and we demonstrate the OSN Network group of people seeing the Information online voting and their personal opinion will be Shared in that OSN Network Scalability and Real information gathered from their personal friend and mutual friend feedback participate in online Voting process. In our experiments End to end user information we know that we can simply identify the fake user are Real user can while users interest for hot voting can be better mined then we further purpose a hybrid RS bagging different approaches to achieve the best top-k hit.
机译:在朋友之间共享信息使用What's Up,Twitter脸书等不同的在线社交网络,不仅是朋友共享个人资料更新,音频,视频,还有更多的朋友。他的直接朋友是共同的朋友,与全球直接联系的在线社交网络是间接的使用在线社交网络共享任何产品的朋友都会被评论,并对它进行独特的挑战和推荐机会进行排名。此实验具有真实的在线投票轨迹,我们演示了OSN网络的一群人看到了信息在线投票,并且他们的个人意见将被共享,因为从他们的个人朋友和共同的朋友反馈中收集的OSN网络可伸缩性和真实信息将参与在线投票过程。在我们的实验“端到端用户信息”中,我们知道我们可以简单地识别出假用户是“真实”用户,而可以更好地挖掘用户对热投票的兴趣,然后我们进一步采用混合RS袋装方法来实现最佳top-k命中。

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