64,693 Hits in 7.8 sec

Contrastive Learning for Sequential Recommendation [article]

Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Bolin Ding, Bin Cui
2021 arXiv   pre-print
To tackle that, inspired by recent advances of contrastive learning techniques in the computer version, we propose a novel multi-task model called Contrastive Learning for Sequential Recommendation (CL4SRec  ...  Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions.  ...  It is a non-personalized approach which recommends the same items for each user.  ... 
arXiv:2010.14395v2 fatcat:2pissecqs5dopo5nderaunptyq

Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation

Yuehua Zhu, Bo Huang, Shaohua Jiang, Muli Yang, Yanhua Yang, Wenliang Zhong
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
The recently proposed Transformer-based models have proved superior in the sequential recommendation, modeling temporal dynamics globally via the remarkable self-attention mechanism.  ...  In this paper, we propose a novel interpretable convolutional self-attention, which efficiently captures both short-and long-term patterns with a progressive attention distribution.  ...  CONCLUSIONS In this paper, we proposed a novel convolutional self-attention and an unsymmetrical positional encoding strategy for the sequential recommendation, namely PAUP.  ... 
doi:10.1145/3477495.3531800 fatcat:53pypr36ira6fathwdrvrtlxey

Match4Rec: A Novel Recommendation Algorithm Based on Bidirectional Encoder Representation with the Matching Task [article]

Lingxiao Zhang, Jiangpeng Yan, Yujiu Yang, Xiu Li
2020 arXiv   pre-print
Recently, the use of self-attention mechanisms and bi-directional architectures have gained much attention.  ...  The sequential recommender system can learn powerful hidden representations of users from successive user-item interactions and dynamic users' preferences.  ...  Furthermore, SASRec outperforms RNN (GRU4Rec+), CNN (Caser) sequential model, on the whole, meaning that the self-attention mechanism is more powerful for sequential feature extraction.  ... 
arXiv:2008.13345v4 fatcat:45rahnjo7vgj7l6psci6kbxs7y

Explanation Guided Contrastive Learning for Sequential Recommendation [article]

Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao
2022 arXiv   pre-print
EC4SRec then combines both self-supervised and supervised contrastive learning over the positive and negative sequences generated by EGA operations to improve sequence representation learning for more  ...  In this work, we address the above problem by proposing Explanation Guided Augmentations (EGA) and Explanation Guided Contrastive Learning for Sequential Recommendation (EC4SRec) model framework.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.  ... 
arXiv:2209.01347v1 fatcat:bwclwt4qqbhkrg7tb2dkx4cfmy

Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations [article]

Hui Fang, Danning Zhang, Yiheng Shu, Guibing Guo
2020 arXiv   pre-print
In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based  ...  However, there is little systematic study on DL-based methods, especially regarding to how to design an effective DL model for sequential recommendation.  ...  ATRank [153] and CSAN [41] combine self-attention with vanilla attention for the sequential recommendation.  ... 
arXiv:1905.01997v3 fatcat:i7hvdiqjpnaupcq2osrblttb4u

Is News Recommendation a Sequential Recommendation Task?

Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li, Yongfeng Huang
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
However, users usually have strong preferences on the temporal diversity of news information and may not tend to click similar news successively, which is very different from many sequential recommendation  ...  In this paper, we study whether news recommendation can be regarded as a standard sequential recommendation problem.  ...  ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China under Grant numbers 2021ZD0113902, U1936216, U1936208, and the Research Initiation Project of Zhejiang Lab under  ... 
doi:10.1145/3477495.3531862 fatcat:idxazcqc65f7zb34abumcqpe4a

Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation [article]

Yongjing Hao, Pengpeng Zhao, Xuefeng Xian, Guanfeng Liu, Deqing Wang, Lei Zhao, Yanchi Liu, Victor S. Sheng
2022 arXiv   pre-print
To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).  ...  Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance.  ...  For instance, to capture both local and global structures, the Graph Contextualized Self-Attention Network (GC-SAN) method combines self-attention with GNN and achieves promising results.  ... 
arXiv:2204.10128v1 fatcat:wxmku22h4fhzlpeimn452tugu4

Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

Liwei Huang, Yutao Ma, Yanbo Liu, Bohong Danny Du, Shuliang Wang, Deyi Li
2022 ACM Transactions on Information Systems  
of users and items simultaneously on the bipartite graph with a self-attention aggregator.  ...  G raph C onvolutional N etwork (PTGCN) for the sequential recommendation.  ...  The self-attention layer models the sequential and temporal influences using a linear combination with adaptive weights.  ... 
doi:10.1145/3511700 fatcat:5jbvfmzbqng7lcegj5eeqk33uu

AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System [article]

Pengyu Zhao, Kecheng Xiao, Yuanxing Zhang, Kaigui Bian, Wei Yan
2020 arXiv   pre-print
In the second step, it progressively investigates useful low-order and high-order feature interactions in the non-sequential interaction space of stage 2.  ...  For efficient and effective NAS, AMER employs the one-shot random search in all three steps.  ...  NARM is a representative for the combination of attention and recurrent neural network. • Caser [67] is a CNN-based recommender system.  ... 
arXiv:2006.05933v1 fatcat:26dtis6nlzcafdbzvu5ek5kjba

Sequential Recommendation via Stochastic Self-Attention [article]

Ziwei Fan, Zhiwei Liu, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, Philip S. Yu
2022 arXiv   pre-print
Sequential recommendation models the dynamics of a user's previous behaviors in order to forecast the next item, and has drawn a lot of attention.  ...  Transformer-based approaches, which embed items as vectors and use dot-product self-attention to measure the relationship between items, demonstrate superior capabilities among existing sequential methods  ...  The comparison between Caser and Transformer-based methods demonstrates the effectiveness of self-attention in sequential modeling for recommendation.  ... 
arXiv:2201.06035v2 fatcat:h27uaasfzjf2ngb4sy2d36rs5e

Temporal Meta-path Guided Explainable Recommendation [article]

Hongxu Chen, Yicong Li, Xiangguo Sun, Guandong Xu, Hongzhi Yin
2021 arXiv   pre-print
This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable  ...  recommendations.  ...  DP190101985, DP170103954, DP200101374 and LP170100891.  ... 
arXiv:2101.01433v1 fatcat:hmwunjchvfashccozecbkswtra

Enhanced Attention Framework for Multi-Interest Sequential Recommendation

Dapeng Yin, Shuang Feng
2022 IEEE Access  
We conduct experiments for the sequential recommendation on three real-world datasets, Amazon, Taobao and MovieLens-1M.  ...  The existing multi-interest sequential recommendation methods adopt the method of self-attention, but it is based on the self-attention of transformer, which lacks the consideration of the correlation  ...  [7] proposed to apply self-attention mechanism to sequential recommendation.  ... 
doi:10.1109/access.2022.3185063 fatcat:dmbsmttvubfbtfngk6hv2yd6h4

Recommender Transformers with Behavior Pathways [article]

Zhiyu Yao, Xinyang Chen, Sinan Wang, Qinyan Dai, Yumeng Li, Tanchao Zhu, Mingsheng Long
2022 arXiv   pre-print
Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations.  ...  RETR can dynamically plan the behavior pathway specified for each user, and sparingly activate the network through this behavior pathway to effectively capture evolving patterns useful for recommendation  ...  First, the non-sequential recommendation method PopRec performs worse than sequential recommendation methods, indicating that capturing the sequential pattern is essential for sequential recommendation  ... 
arXiv:2206.06804v1 fatcat:g2civh3sdja5pf5mtw5mhpxyje

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer [article]

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang
2019 arXiv   pre-print
Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems.  ...  For this purpose, we introduce a Bidirectional Encoder Representations from Transformers for sequential Recommendation (BERT4Rec).  ...  Furthermore, SASRec performs distinctly better than GRU4Rec and GRU4Rec + , suggesting that self-attention mechanism is a more powerful tool for sequential recommendation.  ... 
arXiv:1904.06690v2 fatcat:vivnjneyvjcclaya4ep2slm62m

Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation [article]

Jibang Wu, Renqin Cai, Hongning Wang
2020 arXiv   pre-print
Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems.  ...  More specifically, to dynamically calibrate the relative input dependence from the self-attention mechanism, we deploy multiple parameterized kernel functions to learn various temporal dynamics, and then  ...  ACKNOWLEDGEMENTS We thank all the anonymous reviewers for their helpful comments. This work was partially supported by the National Science Foundation Grant IIS-1553568.  ... 
arXiv:2002.00741v1 fatcat:afvgvbunazcoxdpgi25ugligcy
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