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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) Collaborative Self-Attention Network for Session-based Recommendation
(2) Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach
(3) Discrete Embedding for Latent Networks
(4) Exploiting the Dynamic Mutual Influencefor Predicting Social Event Participation
(5) Improving Sequential Recommendation with Knowledge-Enhanced Memory Network
(6) Next-item Recommendation with Sequential Hypergraphs
(7) Intent Preference Decoupling for User Representation on Online Recommender System
(8) Neural Tensor Model for Learning Multi-Aspect Factorsin Recommender Systems
(9) Memory Augmented Neural Model for Incremental Session-based Recommendation
(10) Learning Hierarchical Category Influence on both Users and Items for Effective Recommendation
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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
Collaborative Self-Attention Network for Session-based Recommendation
Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach
Discrete Embedding for Latent Networks
Exploiting the Dynamic Mutual Influencefor Predicting Social Event Participation
Improving Sequential Recommendation with Knowledge-Enhanced Memory Network
Next-item Recommendation with Sequential Hypergraphs
Intent Preference Decoupling for User Representation on Online Recommender System
Neural Tensor Model for Learning Multi-Aspect Factorsin Recommender Systems
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