Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2020
Abstract
Social in-feed advertising delivers ads that seamlessly fit insidea user’s feed, and allows users to engage in social actions(likes or comments) with the ads. Many businesses payhigher attention to “engagement marketing” that maximizessocial actions, as social actions can effectively promote brandawareness. This paper studies social action prediction for infeedadvertising. Most existing works overlook the social influenceas a user’s action may be affected by her friends’actions. This paper introduces an end-to-end approach thatleverages social influence for action prediction, and focuseson addressing the high sparsity challenge for in-feed ads. Wepropose to learn influence structure that models who tendsto be influenced. We extract a subgraph with the near neighborsa user interacts with, and learn topological features ofthe subgraph by developing structure-aware graph encodingmethods.We also introduce graph attention networks to learninfluence dynamics that models how a user is influenced byneighbors’ actions.We conduct extensive experiments on realdatasets from the commercial advertising platform ofWeChatand a public dataset. The experimental results demonstratethat social influence learned by our approach can significantlyboost performance of social action prediction.
Discipline
Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Thirty-Fourth AAAI Conference on Artificial Intelligence
Identifier
https://doi.org/10.1609/aaai.v34i01.5357
City or Country
New York
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.