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通过实验分析目前推荐系统领域中的一些问题,如实验结果的可复现性,性能提升等。
论文作者给出了一个消极的报告。
(1) SMONE: A Session-based Recommendation Model based on Neighbor Sessions with Similar Probabilistic Intentions
(2) Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback
(3) MLP4Rec: A Pure MLP Architecture for Sequential Recommendations
(4) Self-supervised Graph Neural Networks for Multi-behavior Recommendation
(5) Collaborative Self-Attention Network for Session-based Recommendation
(6) Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach
(7) Discrete Embedding for Latent Networks
(8) Exploiting the Dynamic Mutual Influencefor Predicting Social Event Participation
(9) Improving Sequential Recommendation with Knowledge-Enhanced Memory Network
(10) Next-item Recommendation with Sequential Hypergraphs
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文献基本信息
文献题目
去谷歌学术搜索
Methodological Issues in Recommender Systems Research
文献作者
文献发表年限
2020
文献关键字
消极的、领域进展
摘要描述
博主信息
Nengjun
内部成员
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特征维度处理
基于离散选择的推荐系统模型与算法
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