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Price-aware Recommendation with Graph Convolutional Networks [article]

Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin
2020 arXiv   pre-print
Towards the first difficulty, we propose to model the transitive relationship between user-to-item and item-to-price, taking the inspiration from the recently developed Graph Convolution Networks (GCN)  ...  The key idea is to propagate the influence of price on users with items as the bridge, so as to make the learned user representations be price-aware.  ...  PUP to maximize the revenue is an interesting and important research question which extends price-aware recommendation to value-aware recommendation.  ... 
arXiv:2003.03975v1 fatcat:s67v6cqifjbnzpzqznm6nhdgoe

User-Event Graph Embedding Learning for Context-Aware Recommendation

Dugang Liu, Mingkai He, Jinwei Luo, Jiangxu Lin, Meng Wang, Xiaolian Zhang, Weike Pan, Zhong Ming
2022 Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining  
some existing context-aware recommendation model, where the feature embeddings are directly initialized with the obtained refined embeddings.  ...  a new convolution strategy on the user-event graph, where each intent node acts as a hub to efficiently propagate the information among different features; 3) a recommendation module is equipped to integrate  ...  Graph Neural Network for Recommendation.  ... 
doi:10.1145/3534678.3539458 fatcat:w6ofqkmewbbv5masle7qyfctw4

Learning Hierarchical Review Graph Representations for Recommendation [article]

Yong Liu, Susen Yang, Yinan Zhang, Chunyan Miao, Zaiqing Nie, Juyong Zhang
2021 arXiv   pre-print
Previous review-based recommendation methods usually employ sophisticated compositional models, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to learn semantic representations  ...  In this paper, we propose a novel review-based recommendation model, named Review Graph Neural Network (RGNN).  ...  The graph convolutional neural networks (GCNs) generalize the convolution operation from grid data to graph data.  ... 
arXiv:2004.11588v3 fatcat:r5j5dncjjzhw5jtysqewmy35va

Graph Convolution Machine for Context-aware Recommender System [article]

Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie
2021 arXiv   pre-print
The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.  ...  In this work, we extend the advantages of graph convolutions to context-aware recommender system (CARS, which represents a generic type of models that can handle various side information).  ...  Graph Neural Networks for Recommendation Another relevant research line is to leverage graph neural networks (GNNs) for recommendation.  ... 
arXiv:2001.11402v3 fatcat:ouyq5iwzhnejlfv76aye5xesti

LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation [article]

Yin Zhang, Can Xu, XianJun Wu, Yan Zhang, LiGang Dong, Weigang Wang
2022 arXiv   pre-print
Recently, many efforts have been devoted to improving Tag-aware recommendation systems (TRS) with Graph Convolutional Networks (GCN), which has become new state-of-the-art for the general recommendation  ...  We propose a novel tag-aware recommendation model named Light Folksonomy Graph Collaborative Filtering (LFGCF), which only includes the essential GCN components.  ...  [4] used graph convolutional networks for tag-aware recommendations, and their TGCN model outperformed other state-of-the-art models. Huang et al.  ... 
arXiv:2208.03454v1 fatcat:oofcupy7r5aujklbvz34z5nkme

FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks [article]

Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li
2021 arXiv   pre-print
Second, a fully-connected graph between stocks and a fully-connected graph between sectors are constructed, along with graph attention networks, to learn the latent interactions among stocks and sectors  ...  While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommendation.  ...  capturing short-term and long-term patterns of stock time series. 5) RankLSTM [13] : a state-of-the-art graph neural network-based method that utilizes temporal graph convolution with 16-dim embeddings  ... 
arXiv:2106.10159v1 fatcat:uzr2krgtavcflokl6hwo32yvhq

Advancing computer systems without technology progress

Christos Kozyrakis
2013 2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)  
provisioning and pricing models  Implications for application development Advancing Systems without Technology Progress  Locality-aware parallelism  Specialization  Reduce overprovisioning  Increase  ...  , graph algorithms, …  Scheduling overheads at large-scale  E.g., >1K cores with fine-grain parallelism  HW scheduling: efficiency Vs flexibility Advancing Systems without Technology Progress  ... 
doi:10.1109/ispass.2013.6557164 dblp:conf/ispass/Kozyrakis13 fatcat:futglxnev5bnbfcmxiw54aonyy

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions [article]

Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, Yong Li
2022 arXiv   pre-print
In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems.  ...  We first introduce the background and the history of the development of both recommender systems and graph neural networks.  ...  They design an encoder with GCN on a pre-defined heterogeneous graph to capture price awareness, and a two-branch factorization machine is utilized as the predictor.  ... 
arXiv:2109.12843v2 fatcat:qc5jocddvffwdp7ic5p3bv4aga

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen
2020 IEEE Access  
., ''MV-GCN: Multi-view graph convolutional networks for link prediction,'' proposes a novel multiview graph convolutional neural network (MV-GCN) model based on the Matrix Completion method by simultaneously  ...  a parallel combination of attentionbased bidirectional long short-term memory recurrent neural networks, with attention-based fully convolutional networks.  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

History-Augmented Collaborative Filtering for Financial Recommendations

Baptiste Barreau, Laurent Carlier
2020 Fourteenth ACM Conference on Recommender Systems  
The algorithm, designed with issues specific to the financial world in mind, uses a custom neural network architecture that tackles the non-stationarity of users' and items' behaviors.  ...  It is consequently crucial for recommender systems used in such environments to be able to adapt to these changes.  ...  How graph neural networks behave with dynamic bipartite graphs is to the best of our knowledge yet to be discovered and could lead to an extension of this work.  ... 
doi:10.1145/3383313.3412206 dblp:conf/recsys/BarreauC20 fatcat:fakgftehorhejcs2lhxwi2envy

Graph Meta Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo
2021 arXiv   pre-print
To tackle the above challenges, we propose a Multi-Behavior recommendation framework with Graph Meta Network to incorporate the multi-behavior pattern modeling into a meta-learning paradigm.  ...  In practical recommendation scenarios, users often exhibit various intents which drive them to interact with items with multiple behavior types (e.g., click, tag-as-favorite, purchase).  ...  [41] learns price-aware purchase intent of users with a GCN-based architecture.  ... 
arXiv:2110.03969v1 fatcat:w4mrfiyvabgsvhcu7jzototitu

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

Lifeng Yin, Jianzheng Lu, Guanghai Zheng, Huayue Chen, Wu Deng
2022 Applied Sciences  
In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional  ...  network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce.  ...  This paper mainly studies a multi-task learning recommendation model with a directed graph convolution network (MTL-DGCNR) for the recommendation field of e-commerce.  ... 
doi:10.3390/app12188956 fatcat:klbuaguwqrhlni7eite6np42py

CoVA: Context-aware Visual Attention for Webpage Information Extraction [article]

Anurendra Kumar, Keval Morabia, Jingjin Wang, Kevin Chen-Chuan Chang, Alexander Schwing
2021 arXiv   pre-print
Specifically, we develop a Context-aware Visual Attention-based (CoVA) detection pipeline which combines appearance features with syntactical structure from the DOM tree.  ...  To study the approach we collect a new large-scale dataset of e-commerce websites for which we manually annotate every web element with four labels: product price, product title, product image and background  ...  Fast R-CNN* + GCN [25] : We use graph convolution networks on our graph formulation where node features are the visual representations obtained from Fast R-CNN*.  ... 
arXiv:2110.12320v1 fatcat:qisjuahlz5aqllumz4b7wthb3y

A Review on Graph Neural Network Methods in Financial Applications [article]

Jianian Wang, Sheng Zhang, Yanghua Xiao, Rui Song
2022 arXiv   pre-print
Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks.  ...  With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations.  ...  a time-aware relational attention network.  ... 
arXiv:2111.15367v2 fatcat:o7gfnhlmrnetnl2s2sccjoquje

Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network

Shumin Deng, Ningyu Zhang, Wen Zhang, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen
2019 Companion Proceedings of The 2019 World Wide Web Conference on - WWW '19  
To address these two problems, we propose a novel Knowledge-Driven Temporal Convolutional Network (KDTCN) for stock trend prediction and explanation.  ...  Deep neural networks have achieved promising results in stock trend prediction.  ...  [41] have proposed a deep knowledge-aware network (DKN), incorporating KG to news recommendation. In these knowledge-driven models, eXplainable AI (XAI) is very important.  ... 
doi:10.1145/3308560.3317701 dblp:conf/www/DengZZCPC19 fatcat:tuk3zupapze5nllpb55opzpvji
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