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A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation [article]

Qiang Cui, Shu Wu, Yan Huang, Liang Wang
2018 arXiv   pre-print
Sequential recommendation is one of fundamental tasks for Web applications. Previous methods are mostly based on Markov chains with a strong Markov assumption.  ...  In this work, to deal with this problem, we propose a Hierarchical Contextual Attention-based GRU (HCA-GRU) network. The first level of HCA-GRU is conducted on the input.  ...  Conclusion In this work, we have proposed a novel network called hierarchical contextual attention-based GRU (HCA-GRU) for sequential recommendation.  ... 
arXiv:1711.05114v3 fatcat:c4nmkzhenfbudew43qvfj6ltdq

Hierarchical Transformers for Group-Aware Sequential Recommendation: Application in MOBA Games

Vladimir Araujo, Helem Salinas, Alvaro Labarca, Andrés Villa, Denis Parra
2022 User Modeling, Adaptation, and Personalization  
In this way, HT4Rec provides a flexible and unified attention-based network structure to capture both general and long-term preferences.  ...  It consists of a contextual encoder that generates a character-item representation contextualized by the other participants involved in the game, followed by a sequential encoder that captures sequential  ...  ACKNOWLEDGMENTS The main author thanks Jorge Esteban Martínez for insightful discussions about the DOTA game.  ... 
doi:10.1145/3511047.3537667 dblp:conf/um/AraujoSLVP22 fatcat:fqrzhjx3lvhcxpv53gcs3biyyi

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.  ...  Thus, we propose a Contextualized Temporal Attention Mechanism that learns to weigh historical actions' influence on not only what action it is, but also when and how the action took place.  ...  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

CARAN: A Context-Aware Recency Based Attention Network for Point-of-interest Recommendation

Md. Billal Hossain, Mohammad Shamsul Arefin, Iqbal H. Sarker, Md. Kowsher, Pranab Kumar Dhar, Takeshi Koshiba
2022 IEEE Access  
Since user's historical check-ins are sequential in nature, Recurrent neural network (RNN) based models with context embedding shows promising result for modeling user's mobility.  ...  To address the above shortcomings, we propose a Context-Aware Recency based Attention Network (CARAN) that incorporates weather conditions with spatiotemporal context and gives focus on recently visited  ...  To answer this research question, we present a contextaware recency based attention network (CARAN) for the recommendation of next point-of-interest.  ... 
doi:10.1109/access.2022.3161941 fatcat:dh6gpocwbbgqladu7cdzg4zhm4

Hierarchical Context-aware Recurrent Network for Session-based Recommendation

Youfang Leng, Li Yu
2021 IEEE Access  
[10] proposed a Hierarchical Contextual Attention-based (HCA) network for the sequential recommendation, in which the context is first summarized from several adjacent items by attention mechanism,  ...  HCA [10] : It utilizes a Hierarchical Contextual Attentionbased (HCA) network for the sequential recommendation, in which the context is summarized from several adjacent items by attention mechanism.  ... 
doi:10.1109/access.2021.3069846 fatcat:5rtu6ppnmjffdbfnp3vojplbdy

Towards A Deep Attention-based Sequential Recommender System

Shahpar Yakhchi, Amin Beheshti, Seyed-Mohssen Ghafari, Mehmet Orgun, Guanfeng Liu
2020 IEEE Access  
INDEX TERMS Attention network, dependency modeling, sequential recommender systems.  ...  In order to overcome the above mentioned problems, we propose a novel Deep Attention-based Sequential (DAS) model.  ...  CONCLUSION In this paper, we have proposed a Deep Attention-based Sequential model, DAS, for the next item recommendation problem.  ... 
doi:10.1109/access.2020.3004656 fatcat:3rwlap4csbdqtdae3oeddla5y4

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation [article]

Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang
2021 arXiv   pre-print
However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics  ...  Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services.  ...  Acknowledgments We thank the anonymous reviewers for their constructive feedback and comments.  ... 
arXiv:2110.03996v1 fatcat:qp5o3osmofgttnnas7r6b6lowu

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations [article]

Md. Ashraful Islam, Mir Mahathir Mohammad, Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali
2020 arXiv   pre-print
for a user.  ...  This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.  ...  [48] proposed Geography-aware sequential recommender based on the Self-Attention Network (GeoSAN) uses a geography-aware self-attention network and geography encoder.  ... 
arXiv:2011.10187v1 fatcat:3uampnqerfdvnpuzrxcrsjviwq

Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation

Shoujin Wang, Longbing Cao, Liang Hu, Shlomo Berkovsky, Xiaoshui Huang, Lin Xiao, Wenpeng Lu
2020 IEEE Intelligent Systems  
In this paper, we propose a novel hierarchical attentive transaction embedding (HATE) model to tackle these issues.  ...  A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data.  ...  CONCLUSIONS This work proposes a hierarchical attentive transaction embedding model HATE -a shallow and wide neural network for transaction embedding.  ... 
doi:10.1109/mis.2020.2997362 fatcat:pfzm3goq5jeknc5cxrkhdltvxe

Learning to Structure Long-term Dependence for Sequential Recommendation [article]

Renqin Cai, Qinglei Wang, Chong Wang, Xiaobing Liu
2020 arXiv   pre-print
Sequential recommendation recommends items based on sequences of users' historical actions.  ...  To account for the long-term dependence, GatedLongRec extracts distant actions of top-k related categories to the user's ongoing intent with a top-k gating network, and utilizes a long-term encoder to  ...  [33] proposed a very similar session-based hierarchical model, i.e., Hierarchical Temporal Convolutional Networks, by replacing the in-session RNN with a Temporal Convolutional Network for fast training  ... 
arXiv:2001.11369v1 fatcat:cmdigb3qmjdd5gmje2ozt2k6hm

A Survey on Session-based Recommender Systems [article]

Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Defu Lian
2021 arXiv   pre-print
In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.  ...  Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy.  ...  Yan Zhao for their constructive suggestions on this work. This work was supported by Australian Research Council Discovery Grants (DP180102378, DP190101079 and FT190100734).  ... 
arXiv:1902.04864v3 fatcat:oka5bvibzzbk5oreltrupehaey

Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks [chapter]

Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin
2021 Applications and Practices in Ontology Design, Extraction, and Reasoning  
Naturally, we represent such a scenario as a temporal knowledge graph and compare plain knowledge graph, a taxonomy and a hypergraph embedding approach, as well as a recurrent neural network architecture  ...  This is a fundamentally limiting restriction for many tasks and applications, since the latent state can depend on a) abstract background information, b) the current situational context and c) the history  ...  and sequential, for their use in embedding-based recommender systems.  ... 
doi:10.3233/ssw210046 fatcat:rfsad4zo7zhybdjloyor4zjczu

Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation

Zhao Li, Long Zhang, Chenyi Lei, Xia Chen, Jianliang Gao, Jun Gao
2020 Complexity  
Specifically, we design a network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions.  ...  In this paper, we propose integrating attention mechanism into RNNs for better modeling sequential user behaviors.  ...  [37] propose a recommender system for online communities based on a dynamicgraph-attention neural network. ey model dynamic user behaviors with a recurrent neural network and contextdependent social  ... 
doi:10.1155/2020/6136095 fatcat:dmo4mylq7rhlroekyyrdeb6ucy

2P-AGRCFN: Two Phase Attention Gated Recurrent Context Filtering Network for Sequential Recommender Systems

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
We propose a Two Phase- Attention Gated Recurrent Context Filtering Network (2P-AGRCF) for dealing with user's long-term dependency as well as short-term preferences.  ...  The first phase of 2P-AGRCFN is performed in the input level by constructing a contextual input using certain number of recent input contexts for handling user's short-term interests.  ...  ACKNOWLEDGMENT This research work is performed as part of the Ph.D work in the area of sequence aware recommender system. There is no funding source(s) involved in this research.  ... 
doi:10.35940/ijitee.a4782.119119 fatcat:ipq4rjlmq5gjxlem4zbi4kkt5e

A Knowledge-Aware Attentional Reasoning Network for Recommendation

Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
The proposed KARN not only develops an attention-based RNN to capture the user's history interests from the user's clicked history sequences, but also a hierarchical attentional neural network to reason  ...  In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users' clicked history sequences and path connectivity between users and items for recommendation.  ...  Acknowledgments This work is supported by National Key R&D Program No.2017YFB0803003, and the National Natural Science Foundation of China (No.61202226), We thank all anonymous reviewers for their constructive  ... 
doi:10.1609/aaai.v34i04.6184 fatcat:4sp67naae5ezrhahfoknew6h54
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