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推薦系統(tǒng)作為AI最成功和最重要的應(yīng)用之一，幫助消費(fèi)者更容易地找到相關(guān)的或感興趣的內(nèi)容、商品或者服務(wù)。

對于想學(xué)習(xí)推薦系統(tǒng)的同學(xué)，我整理了WSDM2022最新的包括了序列推薦、跨域推薦、去偏推薦、聯(lián)邦推薦等13篇推薦系統(tǒng)論文，供大家提前領(lǐng)略學(xué)術(shù)前沿趨勢與牛人的最新想法。

跨域推薦

01.RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

02.Personalized Transfer of User Preferences for Cross-domain Recommendation

序列推薦

03.Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

04.Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

去偏推薦

05.It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences are Dynamic

06.Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning

聯(lián)邦推薦

07.PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion

基于圖結(jié)構(gòu)的推薦

08.Profiling the Design Space for Graph Neural Networks based Collaborative Filtering

09.Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

公平性推薦

10.Enumerating Fair Packages for Group Recommendations

基于元學(xué)習(xí)的推薦

11.Long Short-Term Temporal Meta-learning in Online Recommendation

基于強(qiáng)化學(xué)習(xí)的推薦

12.A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising

13.Choosing the Best of All Worlds: Accurate, Diverse, and Novel Recommendations through Multi-Objective Reinforcement Learning

那么多推薦論文，需要怎么去精讀和泛讀呢，能讓你對推薦系統(tǒng)中的各個部分的算法有一個總體的認(rèn)識，我推薦你來聽Pirtuo老師的《歡迎來到推薦的世界》直播課。

—— 老師介紹 ——

—— 直播大綱 ——

7月21日晚上20：00

7月22日晚上20：00

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已預(yù)約的同學(xué)，還額外附贈2022推薦系統(tǒng)paper13篇~

從放棄到精通，這13篇推薦系統(tǒng)論文一定要讀！【附資料】