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Interest aware PeopleRank: Towards effective social-based opportunistic advertising

机译:关注兴趣的PeopleRank:迈向有效的基于社交的机会广告

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Various emerging context aware social-based applications and services assume constant non-disruptive connectivity. Mobile advertisers in such environments want to reach potentially interested users in a given proximity and within a specified short-duration, whether these users are connected to the network or not. While opportunistic forwarding algorithms can be leveraged for forwarding these advertisements, there is little incentive for those not interested in the ad to act as forwarders. Our goal in this paper is to leverage explicit interest, gathered from a user's social profile, and integrate it with social-based opportunistic forwarding algorithms in order to enable soft realtime opportunistic ad delivery in intermittently connected mobile networks. We propose IPeR, a fully distributed interest-aware forwarding algorithm that integrates with PeopleRank to reduce the overall cost and delay while reducing the number of contacted uninterested candidates. Our results, obtained via simulations and validated with real mobility traces coupled with user social data, are promising. In comparison to interest-oblivious socially-aware protocols such as PeopleRank, the IPeR approach reduces the cost to 70% to reach the same delivery ratio, and reduces the ratio of contacted uninterested forwarders by 23%. It also achieves an extra 70% recall and 107% accuracy with only 2% less precision.
机译:各种新兴的基于上下文的基于社交的应用程序和服务都假定持续不断的无中断连接。在这样的环境中,移动广告商希望在给定的距离内并在指定的短期内吸引潜在感兴趣的用户,无论这些用户是否已连接到网络。尽管可以利用机会转发算法来转发这些广告,但是对于那些对广告不感兴趣的人充当转发者的动机很少。本文的目标是利用从用户的社交资料中收集的明确兴趣,并将其与基于社交的机会转发算法集成,以便在间歇性连接的移动网络中实现软实时的机会广告投放。我们提出IPeR,这是一种与PeopleRank集成的完全分布式的兴趣感知转发算法,可以降低总体成本和延迟,同时减少联系的无兴趣候选人的数量。通过仿真获得并通过真实的移动轨迹以及用户社交数据进行验证的我们的结果很有希望。与诸如PeopleRank之类的无需关注利益的社会意识协议相比,IPeR方法将成本降低到70%,以达到相同的交付率,并与不感兴趣的货运代理联系的比例降低了23%。它还可以实现额外的70%召回率和107%的精度,而精度仅降低2%。

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