# Sparsity-aware neural user behavior modeling in online interaction platforms

arXiv: https://arxiv.org/abs/2202.13491 · HTML: https://mlsanity.com/paper/2202.13491/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 292 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [DynGEM: Deep Embedding Method for Dynamic Graphs](https://mlsanity.com/paper/1805.11273/) (2018)
- [PyTorch Distributed: Experiences on Accelerating Data Parallel Training](https://mlsanity.com/paper/2006.15704/) (2020)
- [Dynamic Graph Representation Learning via Self-Attention Networks](https://mlsanity.com/paper/1812.09430/) (2018)
- [Self-Supervised Graph Representation Learning via Global Context Prediction](https://mlsanity.com/paper/2003.01604/) (2020)
- [Motif-based Convolutional Neural Network on Graphs](https://mlsanity.com/paper/1711.05697/) (2017)
- [Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting](https://mlsanity.com/paper/1905.11395/) (2019)
- [Heterogeneous Information Network Embedding for Meta Path based Proximity](https://mlsanity.com/paper/1701.05291/) (2017)
- [Graph Convolutional Matrix Completion](https://mlsanity.com/paper/1706.02263/) (2017)
- [Heterogeneous Network Motifs](https://mlsanity.com/paper/1901.10026/) (2019)
- [On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications](https://mlsanity.com/paper/1908.08572/) (2019)
- [An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms](https://mlsanity.com/paper/1911.06957/) (2019)

## Abstract (arXiv)

Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences. Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data. Despite their enormous potential, we encounter the unique challenge of data sparsity for a vast majority of entities, e.g., sparsity in ground-truth labels for entities and in entity-level interactions (cold-start users, items in the long-tail, and ephemeral groups). In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications. Our problem settings span transductive and inductive learning scenarios, where transductive learning models entities seen during training and inductive learning targets entities that are only observed during inference. We leverage different facets of information reflecting user behavior (e.g., interconnectivity in social networks, temporal and attributed interaction information) to enable personalized inference at scale. Our proposed models are complementary to concurrent advances in neural architectural choices and are adaptive to the rapid addition of new applications in online platforms.