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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

留言

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

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Nengjun 内部成员

博主其他文章

SMONE: A Session-based Recommendation Model based on Neighbor Sessions with Similar Probabilistic Intentions

Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback

MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

Self-supervised Graph Neural Networks for Multi-behavior Recommendation

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

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