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01/30/2023
Designing an offline reinforcement learning objective from scratch
Offline reinforcement learning has developed rapidly over the recent yea...
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12/07/2022
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning
Recent studies have proposed unified user modeling frameworks that lever...
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07/06/2022
Ask Me What You Need: Product Retrieval using Knowledge from GPT-3
As online merchandise become more common, many studies focus on embeddin...
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06/29/2022
Deformable Graph Transformer
Transformer-based models have been widely used and achieved state-of-the...
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05/31/2022
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting
Time series models aim for accurate predictions of the future given the ...
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05/27/2022
Hazard Gradient Penalty for Survival Analysis
Survival analysis appears in various fields such as medicine, economics,...
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03/26/2022
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Graph Neural Networks (GNNs) often suffer from weak-generalization due t...
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11/15/2021
Scaling Law for Recommendation Models: Towards General-purpose User Representations
A recent trend shows that a general class of models, e.g., BERT, GPT-3, ...
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10/15/2021
Intent-based Product Collections for E-commerce using Pretrained Language Models
Building a shopping product collection has been primarily a human job. W...
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09/05/2021
Global-Local Item Embedding for Temporal Set Prediction
Temporal set prediction is becoming increasingly important as many compa...
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05/24/2021
One4all User Representation for Recommender Systems in E-commerce
General-purpose representation learning through large-scale pre-training...
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03/01/2021
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
In recent years, graph neural networks (GNNs) have been widely adopted i...
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01/31/2021
M2FN: Multi-step Modality Fusion for Advertisement Image Assessment
Assessing advertisements, specifically on the basis of user preferences ...
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11/21/2020
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
Probabilistic time-series models become popular in the forecasting field...
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07/16/2020
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Graph neural networks have shown superior performance in a wide range of...
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07/05/2020
Multi-Manifold Learning for Large-scale Targeted Advertising System
Messenger advertisements (ads) give direct and personal user experience ...
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06/26/2020
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
Graph representation learning is gaining popularity in a wide range of a...
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10/06/2019
Which Ads to Show? Advertisement Image Assessment with Auxiliary Information via Multi-step Modality Fusion
Assessing aesthetic preference is a fundamental task related to human co...
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07/24/2019
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation
Graph Neural Networks (GNNs) have been emerging as a promising method fo...
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09/21/2018
Multimodal Dual Attention Memory for Video Story Question Answering
We propose a video story question-answering (QA) architecture, Multimoda...
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07/04/2017