Sequential Recommender SystemSRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.
Zhang Yang,Feng Fuli, Wang Chenxu,He Xiangnan,Wang Meng, Li Yan,Zhang Yongdong
SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.1479-1488, (2020)
We study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community
Cited by4BibtexViews83Links
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Zhao Pengyu, Xiao Kecheng, Zhang Yuanxing,Bian Kaigui, Yan Wei
We introduce AMER for automatic behavior modeling and interaction exploration in recommender systems to relieve the human efforts from feature engineering and architecture engineering
Cited by1BibtexViews43Links
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Jiacheng Li, Yujie Wang,Julian J. McAuley
WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining Houston ..., pp.322-330, (2020)
TiSASRec models the relative time intervals and absolute positions among items to predict future interactions. Extensive experiments on both sparse and dense datasets show that our model outperforms state-of-the-art baselines
Cited by1BibtexViews57Links
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2009-2019, (2020)
We propose a Geographyaware sequential recommender based on the Self-Attention Network for location recommendation
Cited by0BibtexViews176Links
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Yulong Gu,Zhuoye Ding, Shuaiqiang Wang,Lixin Zou, Yiding Liu,Dawei Yin
CIKM '20: The 29th ACM International Conference on Information and Knowledge Management Virt..., pp.2493-2500, (2020)
Multi-gate Mixture-of-Experts to jointly optimize multi-objectives in e-commerce, and uses the Bias Deep Neural Networks to reduce the select bias in implicit feedback
Cited by0BibtexViews19Links
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Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang, Ji-Rong Wen
We introduced a high-quality dataset TG-ReDial for conversational recommender systems, which was constructed by human annotation based on real-world user data
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Ye Tao, Can Wang, Lina Yao, Weimin Li, Yonghong Yu
We propose a novel sequential recommendation approach TRec, which incorporates the item trend information into the consideration for the item prediction
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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.109-118, (2020)
We argue that the temporal evolution of the effects caused by different relations cannot be neglected
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.483-491, (2020)
We evaluate all the methods in terms of recall, normalized discounted cumulative gain, and mean reciprocal rank
Cited by0BibtexViews187Links
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Lin Zheng, Naicheng Guo, Weihao Chen,Jin Yu, Dazhi Jiang
SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.1957-1960, (2020)
The intent of this work is to model the subjective preferences of users in sequential recommendation
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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.1469-1478, (2020)
We have shown that it is possible to learn universal user representations by modeling only unsupervised user sequential behaviors; and it is possible to adapt the learned representations for a variety of downstream tasks
Cited by0BibtexViews108Links
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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.1101-1110, (2020)
We explore the dynamic meaning of items in realworld scenarios and propose a novel next-item recommendation framework empowered by sequential hypergraphs to incorporate the short-term item correlations for dynamic item embedding
Cited by0BibtexViews73Links
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Sijin Zhou, Xinyi Dai, Haokun Chen,Weinan Zhang,Kan Ren,Ruiming Tang,Xiuqiang He,Yong Yu
SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.179-188, (2020)
The comprehensive experiments with a carefullydesigned simulation environment based on two real-world datasets demonstrate that our model can lead to significantly better performance with higher sample efficiency compared to state-of-the-arts
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Ji Wendi, Wang Keqiang,Wang Xiaoling, Chen TingWei, Cristea Alexandra
The user modeling procedure can be viewed as a decision making progress by reading user memory for multiple hops based on the short-term intent
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Sun Yang,Yuan Fajie, Yang Ming, Wei Guoao,Zhao Zhou, Liu Duo
SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.1299-1308, (2020)
We propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed
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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing Brno Czech Republic ..., pp.1443-1452, (2020)
We show that the standard group recommendation approaches are not favorable for sequential recommendations
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ACM Computing Surveys (CSUR), no. 1 (2019)
We provided an extensive review of the most notable works to date on deep learning based recommender system
Cited by533BibtexViews233Links
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Xiang Wang, Dingxian Wang, Canran Xu,Xiangnan He,Yixin Cao,Tat-Seng Chua
national conference on artificial intelligence, (2019)
CKE: Such embedding-based method is tailored for knowledge graphs-enhanced recommendation, which integrates the representations from Matrix Factorization and TransR to enhance the recommendation
Cited by52BibtexViews366Links
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Fei Sun, Jun Liu, Jian Wu, Changhua Pei,Xiao Lin,Wenwu Ou,Peng Jiang
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, (2019): 1441-1450
Considering we only have one ground truth item for each user, Hit Ratio@k is equivalent to Recall@k and proportional to Precision@k; Mean Reciprocal Rank is equivalent to Mean Average Precision
Cited by31BibtexViews33Links
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AAAI, (2019): 53-60
Based on our designed model, we can improve the performance of user-item rating prediction as compare with several stateof-the-art methods, and more importantly, we can provide adaptive recommendation explanations according to the user dynamic preference
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Keywords
Recommender SystemRecommender SystemsCollaborative FilteringData MiningE CommerceSequential RecommendationCollaborationMarkov Decision ProcessData AnalysisHybrid Recommender System
Authors
Julian J. Mcauley
Paper 7
Hui Xiong
Paper 4
Bamshad Mobasher
Paper 3
Ruining He
Paper 3
Hongzhi Yin
Paper 3
Yong Ge
Paper 3
Xing Xie
Paper 3
Jiaxi Tang
Paper 3
Xiangnan He
Paper 3
Keli Xiao
Paper 2