{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/td3-tucker-decomposition-based-dataset","title":"TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation","arxiv_id":"2502.02854","date":"2025-02-05","proceeding":null,"authors":["Jiaqing Zhang","Mingjia Yin","Hao Wang","Yawen Li","Yuyang Ye","Xingyu Lou","Junping Du","Enhong Chen"],"abstract":"In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on large-scale datasets, but this also results in significant training costs. Dataset distillation has emerged as a key solution, condensing large datasets to accelerate model training while preserving model performance. However, condensing discrete and sequentially correlated user-item interactions, particularly with extensive item sets, presents considerable challenges. This paper introduces \\textbf{TD3}, a novel \\textbf{T}ucker \\textbf{D}ecomposition based \\textbf{D}ataset \\textbf{D}istillation method within a meta-learning framework, designed for sequential recommendation. TD3 distills a fully expressive \\emph{synthetic sequence summary} from original data. To efficiently reduce computational complexity and extract refined latent patterns, Tucker decomposition decouples the summary into four factors: \\emph{synthetic user latent factor}, \\emph{temporal dynamics latent factor}, \\emph{shared item latent factor}, and a \\emph{relation core} that models their interconnections. Additionally, a surrogate objective in bi-level optimization is proposed to align feature spaces extracted from models trained on both original data and synthetic sequence summary beyond the na\\\"ive performance matching approach. In the \\emph{inner-loop}, an augmentation technique allows the learner to closely fit the synthetic summary, ensuring an accurate update of it in the \\emph{outer-loop}. To accelerate the optimization process and address long dependencies, RaT-BPTT is employed for bi-level optimization. Experiments and analyses on multiple public datasets have confirmed the superiority and cross-architecture generalizability of the proposed designs. Codes are released at https://github.com/USTC-StarTeam/TD3.","url_abs":"https://arxiv.org/abs/2502.02854v2","url_pdf":"https://arxiv.org/pdf/2502.02854v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"td3-tucker-decomposition-based-dataset","repo_url":"https://github.com/ustc-starteam/td3","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"dataset-distillation","task_name":"Dataset Distillation"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"sequential-recommendation","task_name":"Sequential Recommendation"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"clipped-double-q-learning","method_name":"Clipped Double Q-learning"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"experience-replay","method_name":"Experience Replay"},{"method_slug":"focus","method_name":"Focus"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"td3","method_name":"TD3"},{"method_slug":"target-policy-smoothing","method_name":"Target Policy Smoothing"},{"method_slug":"tucker","method_name":"TuckER"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2502.02854","atlas_url":"https://app.syntology.ai/?focus=2502.02854","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}