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Jul, 2022
基于先验知识的监督个性化排序推荐
SPR:Supervised Personalized Ranking Based on Prior Knowledge for Recommendation
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Chun Yang, Shicai Fan
TL;DR
研究论文提出了一种新的监督个性化排序(SPR)损失函数,改进了常用的点级和对级损失函数的问题,通过利用先前的知识信息,构造<用户、相似用户、正面项目、负面项目>四元组,大大加快了收敛速度,提高了推荐性能。
Abstract
The goal of a
recommendation system
is to model the relevance between each user and each item through the user-item interaction history, so that maximize the positive samples score and minimize negative samples. Currently, two popular
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