Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps

• URL: http://arxiv.org/abs/2006.06427v2
• Date: Mon, 15 Jun 2020 21:47:35 GMT
• Title: Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
• Authors: Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, Suhang Wang
• Abstract summary: We study a novel problem of explainable user engagement prediction for social network Apps. We design an end-to-end neural framework, FATE, which incorporates three key factors that we identify to influence user engagement. FATE is based on a tensor-based graph neural network (GNN), LSTM and a mixture attention mechanism, which allows for (a) predictive explanations based on learned weights across different feature categories, (b) reduced network complexity, and (c) improved performance in both prediction accuracy and training/inference time.
• Score: 40.58156024231199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid growth and prevalence of social network applications (Apps) in recent years, understanding user engagement has become increasingly important, to provide useful insights for future App design and development. While several promising neural modeling approaches were recently pioneered for accurate user engagement prediction, their black-box designs are unfortunately limited in model explainability. In this paper, we study a novel problem of explainable user engagement prediction for social network Apps. First, we propose a flexible definition of user engagement for various business scenarios, based on future metric expectations. Next, we design an end-to-end neural framework, FATE, which incorporates three key factors that we identify to influence user engagement, namely friendships, user actions, and temporal dynamics to achieve explainable engagement predictions. FATE is based on a tensor-based graph neural network (GNN), LSTM and a mixture attention mechanism, which allows for (a) predictive explanations based on learned weights across different feature categories, (b) reduced network complexity, and (c) improved performance in both prediction accuracy and training/inference time. We conduct extensive experiments on two large-scale datasets from Snapchat, where FATE outperforms state-of-the-art approaches by ${\approx}10\%$ error and ${\approx}20\%$ runtime reduction. We also evaluate explanations from FATE, showing strong quantitative and qualitative performance.

Related papers

• Context-Aware Prediction of User Engagement on Online Social Platforms [15.847199578750924]
  We present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight representation of user engagement on online social platforms. We analyze more than 100 million Snapchat sessions from almost 80.000 users. Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement.
  arXiv  Detail & Related papers  (2023-10-23T03:36:35Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Amortised Inference in Bayesian Neural Networks [0.0]
  We introduce the Amortised Pseudo-Observation Variational Inference Bayesian Neural Network (APOVI-BNN) We show that the amortised inference is of similar or better quality to those obtained through traditional variational inference. We then discuss how the APOVI-BNN may be viewed as a new member of the neural process family.
  arXiv  Detail & Related papers  (2023-09-06T14:02:33Z)
• DySuse: Susceptibility Estimation in Dynamic Social Networks [2.736093604280113]
  We propose a task, called susceptibility estimation in dynamic social networks, which is more realistic and valuable in real-world applications. We leverage a structural feature module to independently capture the structural information of influence diffusion on each single graph snapshot. Our framework is superior to the existing dynamic graph embedding models and has satisfactory prediction performance in multiple influence diffusion models.
  arXiv  Detail & Related papers  (2023-08-21T03:28:34Z)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications [0.0]
  Predictive uncertainty supplements model predictions and enables improved functionality of downstream tasks. We tackle this problem by building upon Monte Carlo Dropout (MCDO) models using the Axolotl framework. We conduct experiments on (1) a multi-class classification task using the CIFAR10 dataset, and (2) a more complex human body segmentation task.
  arXiv  Detail & Related papers  (2021-11-11T22:24:15Z)
• Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network [65.11999700562869]
  We propose a novel collaborative prediction unit (CoPU), which aggregates predictions from multiple collaborative predictors according to a collaborative graph. Our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average.
  arXiv  Detail & Related papers  (2021-07-02T08:20:06Z)
• Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data [63.942632088208505]
  Super-Apps have changed the way we think about the interactions between users and commerce. This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
  arXiv  Detail & Related papers  (2021-02-19T15:13:06Z)
• Adversarial Refinement Network for Human Motion Prediction [61.50462663314644]
  Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend. We propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation.
  arXiv  Detail & Related papers  (2020-11-23T05:42:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.