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A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
[article]
2018
arXiv
pre-print
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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
2022
User Modeling, Adaptation, and Personalization
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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]
2020
arXiv
pre-print
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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
2022
IEEE Access
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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
2021
IEEE Access
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[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
2020
IEEE Access
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2020
IEEE Intelligent Systems
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2020 pre-print arXiv:2006.04530v1 fatcat:iyjklx36jvdfbai4thnqbzpg4a
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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2021
Applications and Practices in Ontology Design, Extraction, and Reasoning
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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
2020
Complexity
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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
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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
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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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