【推荐系统】AAAI2022推荐系统论文集锦
2022年第36屆人工智能頂級(jí)會(huì)議AAAI論文列表已經(jīng)放出,此次會(huì)議共收到9251篇論文提交,其中9020篇論文被審稿。最終錄取篇數(shù)為1349篇,錄取率為可憐的15%。由于境外疫情形勢(shì)依然嚴(yán)峻,大會(huì)將在2月22日到3月1日在線上進(jìn)行舉辦。
較之歷年接受率來(lái)說(shuō),今年的錄取率可以說(shuō)是斷崖式下跌。下圖對(duì)2017年至今年的投稿量以及接受率進(jìn)行了可視化,可以說(shuō)今年的投稿量之多與接受率之低形成了鮮明的對(duì)比。
關(guān)于對(duì)頂級(jí)會(huì)議歷年論文的分析與整理可點(diǎn)擊下方鏈接:
AAAI2021推薦系統(tǒng)論文清單
AAAI2020推薦系統(tǒng)論文集錦
CIKM2021推薦系統(tǒng)論文集錦
RecSys2021推薦系統(tǒng)論文集錦
與往年的慣例相同,我們分析了今年接收論文的標(biāo)題,可以發(fā)現(xiàn)以下結(jié)論:
深度學(xué)習(xí)技術(shù)仍然是比較火熱的技術(shù)之一;
對(duì)圖數(shù)據(jù)的研究依然是大家關(guān)注的數(shù)據(jù)形式之一;
自監(jiān)督學(xué)習(xí)、半監(jiān)督學(xué)習(xí)、多智能體、表示學(xué)習(xí)是大家主要使用的學(xué)習(xí)范式;
機(jī)器學(xué)習(xí)應(yīng)用如目標(biāo)檢測(cè)、文本分類、語(yǔ)義分割等是目前大家比較關(guān)注的方向。
完整版清單可從官網(wǎng)下載查看。
https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf
接下來(lái),特意從1349篇論文中篩選出與推薦系統(tǒng)相關(guān)的15篇文章供大家欣賞(去年的推薦系統(tǒng)論文文章的比例為33/1692),提前領(lǐng)略學(xué)術(shù)前沿趨勢(shì)與牛人的最新想法。
1. Meta-Learning for Online Update of Recommender Systems
Minseok Kim, Hwanjun Song, Yooju Shin, Dongmin Park, Kijung Shin, Jae-Gil Lee
https://minseokkim.net/publication/2022melon_aaai/2022melon_aaai.pdf
2.?DiPS: Differentiable Policy for Sketching in Recommender Systems
Aritra Ghosh, Saayan Mitra, Andrew Lan
https://arxiv.org/pdf/2112.07616
3.?Low-pass Graph Convolutional Network for Recommendation
Wenhui Yu, Zixin Zhang, Zheng Qin
4.?Online certification of preference-based fairness for personalized recommender systems
Virginie Do, Sam Corbett-Davies, Jamal Atif, Nicolas Usunier
https://arxiv.org/pdf/2104.14527
5.?Modeling Attrition in Recommender Systems with Departing Bandits
Omer Ben-Porat, Lee Cohen, Liu Leqi, Zachary Lipton, Yishay Mansour
6.?A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations
Krishna P Neupane, Ervine Zheng, Yu Kong, Qi Yu
7.?Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
Hao Wang, Yifei Ma, Hao Ding, Yuyang Wan
8.?Multi-view Intent Disentangle Graph Networks for Bundle Recommendation
Sen Zhao, Wei Wei, Ding Zou, Xian-Ling Mao
9.?SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation
Wanjie Tao, Yu Li, Liangyue Li, Zulong Chen, Hong Wen, Peilin Chen, Tingting Liang, Quan Lu
10.?Leaping Through Time with Gradient-based Adaptation for Recommendation
Nuttapong Chairatanakul, Hoang NT, Xin Liu, Tsuyoshi Murata
https://arxiv.org/pdf/2112.05914
11.?Cross-Task Knowledge Distillation in Multi-Task Recommendation
Chenxiao Yang, Junwei Pan, Xiaofeng Gao, Tingyu Jiang, Dapeng Liu, Guihai Chen
12.?FPAdaMetric: False-positive-aware Adaptive Metric Learning for Session-based Recommendation
Jongwon Jeong, Jeong Choi, Hyunsouk Cho, Sehee Chung
13.?Offline Interactive Recommendation with Natural-Language Feedback
Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin
14.?Learning the Optimal Recommendation from Explorative Users
Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
https://arxiv.org/pdf/2110.03068
15.?Obtaining Calibrated Probabilities with Personalized Ranking Models
Wonbin Kweon, SeongKu Kang, Hwanjo Yu
通過(guò)整理發(fā)現(xiàn),此次會(huì)議接收的推薦系統(tǒng)相關(guān)論文主要涉及基于元學(xué)習(xí)的推薦系統(tǒng)2篇,序列化推薦5篇,基于強(qiáng)化學(xué)習(xí)的推薦系統(tǒng)4篇以及冷啟動(dòng)推薦2篇。
往期精彩回顧適合初學(xué)者入門人工智能的路線及資料下載中國(guó)大學(xué)慕課《機(jī)器學(xué)習(xí)》(黃海廣主講)機(jī)器學(xué)習(xí)及深度學(xué)習(xí)筆記等資料打印機(jī)器學(xué)習(xí)在線手冊(cè)深度學(xué)習(xí)筆記專輯《統(tǒng)計(jì)學(xué)習(xí)方法》的代碼復(fù)現(xiàn)專輯 AI基礎(chǔ)下載本站qq群955171419,加入微信群請(qǐng)掃碼:
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