Cover Image

Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Juyong, Zhang, Peiyan, Luo, Yingtao, Li, Chaozhuo, Kim, Jae Boum, Zhang, Kai, Wang, Senzhang, Kim, Sunghun, Yu, Philip S.
Format:	Preprint
Published:	2021
Subjects:	Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
by: Zhou, Peilin, et al.
Published: (2024)

TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
by: Zhang, Peiyan, et al.
Published: (2023)

GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
by: Zhang, Peiyan, et al.
Published: (2024)

Equivariant Contrastive Learning for Sequential Recommendation
by: Zhou, Peilin, et al.
Published: (2022)

Attention Calibration for Transformer-based Sequential Recommendation
by: Zhou, Peilin, et al.
Published: (2023)

High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs
by: Zhang, Peiyan, et al.
Published: (2024)

DTRec: Learning Dynamic Reasoning Trajectories for Sequential Recommendation
by: Shao, Yifan, et al.
Published: (2025)

Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning
by: Zhang, Lingzi, et al.
Published: (2023)

Improve Temporal Awareness of LLMs for Sequential Recommendation
by: Chu, Zhendong, et al.
Published: (2024)

Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model
by: Wang, Sirui, et al.
Published: (2024)

Data Augmentation for Sequential Recommendation: A Survey
by: Dang, Yizhou, et al.
Published: (2024)

Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation
by: Zhang, Lingzi, et al.
Published: (2024)

Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
by: Li, Li, et al.
Published: (2024)

Facet-Aware Multi-Head Mixture-of-Experts Model with Text-Enhanced Pre-training for Sequential Recommendation
by: Liu, Mingrui, et al.
Published: (2026)

ID-centric Pre-training for Recommendation
by: Wu, Yiqing, et al.
Published: (2024)

PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information
by: Song, Chonggang, et al.
Published: (2024)

Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models
by: Wang, Yuhao, et al.
Published: (2024)

The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation
by: Qu, Zekai, et al.
Published: (2024)

To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models
by: Wang, Zhefan, et al.
Published: (2024)

Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential Recommendation
by: Sun, Wenqi, et al.
Published: (2024)

Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework
by: Han, Ruidong, et al.
Published: (2024)

Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure
by: Zhao, Ziqi, et al.
Published: (2025)

LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation
by: Sun, Yuqi, et al.
Published: (2025)

A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot Transfer
by: Wang, Junting, et al.
Published: (2024)

Bidirectional Knowledge Distillation for Enhancing Sequential Recommendation with Large Language Models
by: Wu, Jiongran, et al.
Published: (2025)

Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
by: Dang, Yizhou, et al.
Published: (2024)

Augmenting Sequential Recommendation with Balanced Relevance and Diversity
by: Dang, Yizhou, et al.
Published: (2024)

Intent-Enhanced Data Augmentation for Sequential Recommendation
by: Chen, Shuai, et al.
Published: (2024)

Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
by: Dang, Yizhou, et al.
Published: (2025)

AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation
by: Zhang, Kaike, et al.
Published: (2025)

Tail-Aware Data Augmentation for Long-Tail Sequential Recommendation
by: Dang, Yizhou, et al.
Published: (2026)

SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation
by: Zhang, Chi, et al.
Published: (2024)

FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation
by: Kim, WooJoo, et al.
Published: (2026)

GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training
by: Li, Yang, et al.
Published: (2023)

RaSeRec: Retrieval-Augmented Sequential Recommendation
by: Zhao, Xinping, et al.
Published: (2024)

Data Watermarking for Sequential Recommender Systems
by: Zhang, Sixiao, et al.
Published: (2024)

Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
by: Cui, Ziqiang, et al.
Published: (2025)

Pre-train and Fine-tune: Recommenders as Large Models
by: Jiang, Zhenhao, et al.
Published: (2025)

Improving Sequential Recommendations with LLMs
by: Boz, Artun, et al.
Published: (2024)

Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation
by: Wang, Wei, et al.
Published: (2025)