As influencers play considerable roles in social media marketing, companies
increase the budget for influencer marketing. Hiring effective influencers is
crucial in social influencer marketing, but it is challenging to find the right
influencers among hundreds of millions of social media users. In this paper, we
propose InfluencerRank that ranks influencers by their effectiveness based on
their posting behaviors and social relations over time. To represent the
posting behaviors and social relations, the graph convolutional neural networks
are applied to model influencers with heterogeneous networks during different
historical periods. By learning the network structure with the embedded node
features, InfluencerRank can derive informative representations for influencers
at each period. An attentive recurrent neural network finally distinguishes
highly effective influencers from other influencers by capturing the knowledge
of the dynamics of influencer representations over time. Extensive experiments
have been conducted on an Instagram dataset that consists of 18,397 influencers
with their 2,952,075 posts published within 12 months. The experimental results
demonstrate that InfluencerRank outperforms existing baseline methods. An
in-depth analysis further reveals that all of our proposed features and model
components are beneficial to discover effective influencers.

本文提出 InfluencerRank，使用图卷积神经网络和注意力机制的循环神经网络来排名社交媒体上的影响者，实验结果显示其优于现有的基准方法，可以作为发现高效影响者的有效工具。

通过图卷积注意力循环神经网络发现有效的影响者

InfluencerRank: Discovering Effective Influencers via Graph  Convolutional Attentive Recurrent Neural Networks

Social media marketing plays a vital role in promoting brand and product
values to wide audiences. In order to boost their advertising revenues, global
media buying platforms such as Facebook Ads constantly reduce the reach of
branded organic posts, pushing brands to spend more on paid media ads. In order
to run organic and paid social media marketing efficiently, it is necessary to
understand the audience, tailoring the content to fit their interests and
online behaviours, which is impossible to do manually at a large scale. At the
same time, various personality type categorization schemes such as the
Myers-Briggs Personality Type indicator make it possible to reveal the
dependencies between personality traits and user content preferences on a wider
scale by categorizing audience behaviours in a unified and structured manner.
This problem is yet to be studied in depth by the research community, while the
level of impact of different personality traits on content recommendation
accuracy has not been widely utilised and comprehensively evaluated so far.
Specifically, in this work we investigate the impact of human personality
traits on the content recommendation model by applying a novel
personality-driven multi-view content recommender system called Personality
Content Marketing Recommender Engine, or PersiC. Our experimental results and
real-world case study demonstrate not just PersiC's ability to perform
efficient human personality-driven multi-view content recommendation, but also
allow for actionable digital ad strategy recommendations, which when deployed
are able to improve digital advertising efficiency by over 420% as compared to
the original human-guided approach.

本研究旨在研究人格特质对内容推荐模型的影响，通过一个名为 PersiC 的创新的基于人格特质的多视角内容推荐引擎，进行实验和现实案例研究，结果表明它不仅能够有效地具有人格驱动的多视角内容推荐能力，而且能够提出行之有效的数字广告策略建议，将数字广告效率提高了 420% 以上，相较于最初的人为指导方法。