BriefGPT.xyz
May, 2021
一类协同过滤的用户和物品表示的引导
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
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Dongha Lee, SeongKu Kang, Hyunjun Ju, Chanyoung Park, Hwanjo Yu
TL;DR
本文提出了一种名为BUIR的新型OCCF框架,在没有负采样的情况下有效缓解了数据稀疏性问题,并利用随机数据增强来进一步提高模型性能。实验结果表明,BUIR相比于已有的基线方法显著提高了推荐性能。
Abstract
The goal of
one-class collaborative filtering
(
occf
) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of positive user-item interactions
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