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Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel

Xiaomin Fang, Rong Pan, Guoxiang Cao, Xiuqiang He, Wenyuan Dai
Personalized tag recommendation systems recommend a list of tags to a user when he is about to annotate an item. It exploits the individual preference and the characteristic of the items.  ...  In this paper, we propose a novel method for personalized tag recommendation, which can be considered as a nonlinear extension of Canonical Decomposition.  ...  Acknowledgements We would like to thank the many referees of the previous version of this paper for their extremely useful suggestions and comments.  ... 
doi:10.1609/aaai.v29i1.9214 fatcat:zt3opneabne27bwf2fmbqz2jmm

LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text [article]

Guangneng Hu, Yu Zhang, Qiang Yang
2018 arXiv   pre-print
Collaborative filtering (CF) is the key technique for recommender systems.  ...  The proposed framework, called LCMR, is based on memory networks and consists of local and centralized memories for exploiting content information and interaction data, respectively.  ...  Similarly for the task of item recommendation, each user is only interested in identifying top-N items.  ... 
arXiv:1804.06201v2 fatcat:a2iemo2aenc2toqhj65j7n6hai

Top-N-Targets-Balanced Recommendation Based on Attentional Sequence-to-Sequence Learning

Xingkai Wang, Yiqiang Sheng, Haojiang Deng, Zhenyu Zhao
2019 IEEE Access  
INDEX TERMS Long short-term memory, sequential recommendation, attentional sequence-to-sequence learning, top-N-targets-balanced recommendation.  ...  Understanding the dynamics of users' behaviors and preferences can improve the performance of recommendation system.  ...  ACKNOWLEDGMENT The authors would like to thank the editor and the anonymous reviewers for constructive suggestions. They their families and friends who support them.  ... 
doi:10.1109/access.2019.2937557 fatcat:cuhnxlylxjhx5ndu4phljv26ce

Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

Huazhen Liu, Wei Wang, Yihan Zhang, Renqian Gu, Yaqi Hao, Ahmed Mostafa Khalil
2022 Computational Intelligence and Neuroscience  
Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system.  ...  The combination of the two can effectively improve the performance of the recommendation system.  ...  performance and stability, which proves the effectiveness of the EINMF for top-N recommendation tasks.  ... 
doi:10.1155/2022/9593957 pmid:35047036 pmcid:PMC8763527 fatcat:vwzwthw64jesrflazgxiy6gwta

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
Extensive hyperparameters experiments and ablation studies on three real-world datasets show that LFGCF uses fewer parameters and significantly outperforms most baselines for the tag-aware top-N recommendations  ...  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  ...  for Top-K recommendation tasks.  ... 
arXiv:2208.03454v1 fatcat:oofcupy7r5aujklbvz34z5nkme

Deep Collaborative Filtering: A Recommendation Method for Crowdfunding Project Based on the Integration of Deep Neural Network and Collaborative Filtering

Pei Yin, Jing Wang, Jun Zhao, Huan Wang, Hongcheng Gan, Wei Liu
2022 Mathematical Problems in Engineering  
collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation.  ...  In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of  ...  It calculates the ratio of fundraising items in the top-K recommendation list that belong to the test set, as shown in equation HR � n N , ( 17 ) where n represents the number of items in the test set  ... 
doi:10.1155/2022/4655030 fatcat:6nadmi32g5hrzaurn3bukqazzi

Light Graph Convolutional Collaborative Filtering with Multi-aspect Information

Denghua Mei, Niu Huang, Xin Li
2021 IEEE Access  
INDEX TERMS Recommender systems, graph convolutional network, representation learning, multi-aspect information.  ...  Finally, the representations of all aspects and all propagation layers are fused for recommendation. We apply LGC-ACF to three datasets: Movielens, Amazon, and Taobao.  ...  PinSage [23] is the first application of GCN to a web-scale recommendation system.  ... 
doi:10.1109/access.2021.3061915 fatcat:yeus775p5nebvcrpjbzqse7f7q

Holographic Factorization Machines for Recommendation

Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, Tran Dang Quang Vinh
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks.  ...  Additionally, we propose a neural adaptation of HFM which enhances its capability to handle nonlinear structures.  ...  (Nickel, Rosasco, and Poggio 2016) proposed HOLE, a knowledge graph embedding that exploits circular correlation for learning entity relationships.  ... 
doi:10.1609/aaai.v33i01.33015143 fatcat:gytn74wgunhwrprnme4cgtbfxe

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation [article]

Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua
2020 arXiv   pre-print
recommend a single item (e.g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items.Hence, performing high-quality personalized outfit recommendation  ...  Towards this end, we develop a new framework, Hierarchical Fashion Graph Network(HFGN), to model relationships among users, items, and outfits simultaneously.  ...  After propagating information within the hierarchical fashion graph, we allow the information flow from the bottom to the top levels, exploiting the complex relationships among items, outfits, and users  ... 
arXiv:2005.12566v1 fatcat:hby7ghuchvbifncxtrf7ohso6i

A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling

Jibing Gong, Xinghao Zhang, Qing Li, Cheng Wang, Yaxi Song, Zhiyong Zhao, Shuli Wang
2021 Applied Sciences  
Aiming at the potential information acquisition problem from assorted feedback, we propose a new top-N recommendation method MFDNN for Heterogeneous Information Networks (HINs).  ...  system.  ...  On the other hand, DNN is a nonlinear model that can mine the potential nonlinear relationship characteristics of user-item data.  ... 
doi:10.3390/app11167418 fatcat:phefx7z2lzhkjo2yl7im5zbx2y

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Yang Su, Xiangwei Kong, Guobao Liu, Jian Su
2021 Security and Communication Networks  
To accurately predict the click-through rate (CTR) and use it for ad recommendation, we propose a deep attention AD popularity prediction model (DAFCT) based on label recommendation technology and collaborative  ...  First, we construct an Attention-LSTM model to capture the popularity trends and exploit the temporal information based on users' feedback; finally, we use the concatenate method to fuse temporal information  ...  In this paper, the data set is divided into two parts: a training set and test set, where the training set accounts for 80% and the test set accounts for 20%. e recommendation list is output by Top-N,  ... 
doi:10.1155/2021/9940232 fatcat:4aaqsvb5rvbcfahpombwild3qy

Collaboration Based Multi-Label Propagation for Fraud Detection

Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui, Weiwei Liu, Tianlei Hu, Gang Chen
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
We first introduce a general-purpose version that involves collaboration technique to exploit label correlations.  ...  systems.  ...  Figure 3 : 3 Performance of Top-k recommendations in terms of Hit@k (3a-3d) and NDCG@k (3e-3h) with error bars.  ... 
doi:10.24963/ijcai.2020/339 dblp:conf/ijcai/Chen020 fatcat:vfbygf3qsrg6nd5nistvl7jm2q


Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis
2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '19  
With the help of GCN, we could capture nonlinear interactions and exploit measured similarities. Moreover, we define a margin control loss function to reduce the effect of sparsity.  ...  to capture nonlinear associations between diseases and genes.  ...  Because we are usually interested in a few top-ranked items, NDCG@N is used to compare the top-N recommendation performance.  ... 
doi:10.1145/3292500.3330912 dblp:conf/kdd/HanYZSLZ0K19 fatcat:jlescbaxj5gejdl3hx64ojndje

SiReN: Sign-Aware Recommendation Using Graph Neural Networks [article]

Changwon Seo, Kyeong-Joong Jeong, Sungsu Lim, Won-Yong Shin
2022 arXiv   pre-print
Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems.  ...  In this study, we present SiReN, a new sign-aware recommender system based on GNN models.  ...  We evaluate the accuracy of top-K recommendation when K is set to 10 for all datasets. First, we recall the original SiReN method employing MLP for G n , dubbed SiReN MLP-G n .  ... 
arXiv:2108.08735v2 fatcat:bd2rfl4pbbc35aj5uapy2cdhj4

A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Yonghong Yu, Weiwen Qian, Li Zhang, Rong Gao
2022 Sensors  
component and abandons the feature transformation and nonlinear activation components.  ...  Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance.  ...  A recommendation system (RS) [1] is an effective tool for alleviating information overload.  ... 
doi:10.3390/s22197122 fatcat:u6bscs5blfdnxo3rool5tjqe3y
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