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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) A Fairness-Aware Recommender System Based On Genetic Algorithm For User, Provider and Platform Optimization
(2) SMONE: A Session-based Recommendation Model based on Neighbor Sessions with Similar Probabilistic Intentions
(3) Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback
(4) MLP4Rec: A Pure MLP Architecture for Sequential Recommendations
(5) Self-supervised Graph Neural Networks for Multi-behavior Recommendation
(6) Collaborative Self-Attention Network for Session-based Recommendation
(7) Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach
(8) Discrete Embedding for Latent Networks
(9) Exploiting the Dynamic Mutual Influencefor Predicting Social Event Participation
(10) Improving Sequential Recommendation with Knowledge-Enhanced Memory Network
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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
博主其他文章
Towards Improving Top-N Recommendation by Generalization of SLIM
Top-N Recommendation with Novel Rank Approximation
Top-N Recommender System via Matrix Completion
LibRec
Compression-Based Selective Sampling for Learning to Rank
LETOR: Benchmark Datasets for Learning to Rank
BPR: Bayesian Personalized Ranking from Implicit Feedback
GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE
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