IJCAI'20最新推荐系统论文聚焦

时间过得真快，仿佛昨天刚整理完IJCAI'19最新推荐系统相关论文，今天就要开始整理2020年IJCAI中关于推荐系统的论文了。

首先，我们来说一说今年的IJCAI。好像一直在被吐槽，从一开始的灭霸操作5147 篇文章直接Summary Reject掉2191篇文章（拒稿率42%），这一操作让许多投稿人一脸懵逼。可想而知今年的接收率达到了历史最低，仅为12.6%，于是小磊把近5年的接收率做了个图表，发现一路走低，看来AI女神要回归了。大家可以搞个神经网络来进行下一年的接收率预测了（逃）。另一个被吐槽的操作是今年的会议大概率不开了，要拖到明年1月份，到时候许多新的idea可能已经成了明日黄花。

然后，再分析本次的论文接收列表，分别对论文标题和作者进行统计分析，并制作了词云如下。

通过对标题进行可视化发现，深度神经网络（Deep, Neural, Network）依然是主力；图(Graph)作为数据呈现的主要形式被广泛研究；大部分研究者利用的技术依然是Adversarial、Attention等。

通过对作者进行可视化发现，研究人员中出现很大比例的姓氏为张，王，李，刘，杨，陈等（没错，我也姓张），这让我不禁想到了《百家姓》，赵钱孙李周吴郑王....，仔细一瞅，其中也可以零星看到几个外国名字，比如George，Andrea等，可见中国研究者为人工智能事业做出了多么巨大的贡献，发表（灌水）了许多该领域的顶级论文。

最后，涉及到推荐系统的文章为17篇，主要是在Machine Learning Track中，所涉及的主题主要包括序列推荐，POI推荐，可解释推荐，跨域推荐，新闻推荐等。

Sequential RS

1. Adversarial Oracular Seq2seq Learning for Sequential RecommendationPengyu Zhao, Tianxiao Shui, Yuanxing Zhang, Kecheng Xiao, Kaigui Bian

2. Collaborative Self-Attention Network for Session-based RecommendationAnjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, Victor S. Sheng

3. Memory Augmented Neural Model for Incremental Session-based RecommendationFei Mi, Boi Faltings

Cross-Domain RS

1. A Graphical and Attentional Framework for Dual-Target Cross-Domain RecommendationFeng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Xiaolin Zheng

2. Learning Personalized Itemset Mapping for Cross-Domain RecommendationYinan Zhang, Yong Liu, Peng Han, Chunyan Miao, Lizhen Cui, Baoli Li, Haihong Tang

POI RS

1. Contextualized Point-of-Interest RecommendationPeng Han, Zhongxiao Li, Yong Liu, Peilin Zhao, Jing Li, Hao Wang, Shuo Shang

2. Discovering Subsequence Patterns for Next POI RecommendationKangzhi Zhao, Yong Zhang, Hongzhi Yin, Jin Wang, Kai Zheng, Xiaofang Zhou, Chunxiao Xing

3. An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-insLu Zhang, Zhu Sun, Jie Zhang, Yu Lei, Chen Li, Ziqing Wu, Horst Kloeden, Felix Klanner

Explainable RS

1. Explainable Recommendation via Interpretable Feature Mapping and Evaluation of ExplainabilityDeng Pan, Xiangrui Li, Xin Li, Dongxiao Zhu

2. Synthesizing Aspect-Driven Recommendation Explanations from ReviewsTrung-Hoang Le, Hady W. Lauw

3. Towards Explainable Conversational RecommendationZhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, Enhong Chen

News RS

1. User Modeling with Click Preference and Reading Satisfaction for News RecommendationChuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang

2. HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural NetworkRui Liu, Huilin Peng, Yong Chen, Dell Zhang

Cold-Start In RS

1. Internal and Contextual Attention Network for Cold-start Multi-channel Matching in RecommendationRuobing Xie, Zhijie Qiu, Jun Rao, Yi Liu, Bo Zhang, Leyu Lin

General RS

1. Deep Feedback Network for RecommendationRuobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, Leyu Lin

2. Intent Preference Decoupling for User Representation on Online Recommender SystemZhaoyang Liu, Haokun Chen, Fei Sun, Xu Xie, Jinyang Gao, Bolin Ding, Yanyan Shen

3. Neural Tensor Model for Learning Multi-Aspect Factors in Recommender SystemsHuiyuan Chen, Jing Li

 3   推荐阅读

[0].WWW2020推荐系统论文合集(已分类整理)
[1].ECAI2020推荐系统论文聚焦[2].深度总结 | 知识蒸馏在推荐系统中的应用[3].当推荐系统邂逅深度学习[4].推荐系统领域中那些巧妙运用的idea

---

喜欢的话点个在看吧👇