吉林大学学报（理学版）2024，Vol.62Issue(1)：92-99,8.DOI:10.13413/j.cnki.jdxblxb.2022439
融合协同过滤的神经Bandits推荐算法
Neural Bandits Recommendation Algorithm Based on Collaborative Filtering
摘要
Abstract
Aiming at the problems of the limitations of data sparsity and"cold start"on collaborative filtering and the inapplicability of the existing collaborative multi-armed Bandit algorithm to nonlinear reward functions,we proposed a neural Bandit recommendation algorithm COEENet,which combined collaborative filtering.Firstly,it adopted a dual neural network structure to learn expected rewards and potential gains.Secondly,we considered the collaborative effect of neighbors.Finally,a decision-maker was constructed to make the final decision.The experimental results show that the proposed method is superior to the four baseline algorithms in cumulative regret,and has a good recommendation effect.关键词
协同过滤/多臂老虎机算法/推荐系统/冷启动Key words
collaborative filtering/multi-armed Bandit algorithm/recommendation system/cold start分类
信息技术与安全科学引用本文复制引用
张婷婷,欧阳丹彤,孙成林,白洪涛..融合协同过滤的神经Bandits推荐算法[J].吉林大学学报（理学版）,2024,62(1):92-99,8.基金项目
吉林省自然科学基金(批准号:20210101181JC). （批准号:20210101181JC）
