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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

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

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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