The Experts below are selected from a list of 89781 Experts worldwide ranked by ideXlab platform
Philip S Yu - One of the best experts on this subject based on the ideXlab platform.
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jscn joint spectral convolutional network for cross domain recommendation
International Conference on Big Data, 2019Co-Authors: Lei Zheng, Jiawei Zhang, Philip S YuAbstract:Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant User Representation with a domain adaptive User mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant User mapping. The domain adaptive User mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9.2% improvement on recall and 36.4% improvement on MAP compared with state-of-the-art methods. Our code is available online 1.1https://github.com/JimLiu96/JSCN
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jscn joint spectral convolutional network for cross domain recommendation
arXiv: Learning, 2019Co-Authors: Lei Zheng, Jiawei Zhang, Philip S YuAbstract:Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a \textbf{J}oint \textbf{S}pectral \textbf{C}onvolutional \textbf{N}etwork (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant User Representation with a domain adaptive User mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant User mapping. The domain adaptive User mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on $24$ Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with $9.2\%$ improvement on recall and $36.4\%$ improvement on MAP compared with state-of-the-art methods. Our code is available online ~\footnote{this https URL}.
Lei Zheng - One of the best experts on this subject based on the ideXlab platform.
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jscn joint spectral convolutional network for cross domain recommendation
International Conference on Big Data, 2019Co-Authors: Lei Zheng, Jiawei Zhang, Philip S YuAbstract:Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant User Representation with a domain adaptive User mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant User mapping. The domain adaptive User mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9.2% improvement on recall and 36.4% improvement on MAP compared with state-of-the-art methods. Our code is available online 1.1https://github.com/JimLiu96/JSCN
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jscn joint spectral convolutional network for cross domain recommendation
arXiv: Learning, 2019Co-Authors: Lei Zheng, Jiawei Zhang, Philip S YuAbstract:Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a \textbf{J}oint \textbf{S}pectral \textbf{C}onvolutional \textbf{N}etwork (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant User Representation with a domain adaptive User mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant User mapping. The domain adaptive User mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on $24$ Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with $9.2\%$ improvement on recall and $36.4\%$ improvement on MAP compared with state-of-the-art methods. Our code is available online ~\footnote{this https URL}.
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mars memory attention aware recommender system
IEEE International Conference on Data Science and Advanced Analytics, 2019Co-Authors: Lei Zheng, Sihong Xie, Vahid Noroozi, Bowen DongAbstract:In this paper, we study the problem of modeling Users' diverse interests. Previous methods usually learn a fixed User Representation, which has a limited ability to represent distinct interests of a User. In order to model Users' various interests, we propose a Memory Attention-aware Recommender System (MARS). MARS utilizes a memory component and a novel attentional mechanism to learn deep adaptive User Representations. Trained in an end-to-end fashion, MARS adaptively summarizes Users' interests. In the experiments, MARS outperforms seven state-of-the-art methods on three real-world datasets in terms of recall and mean average precision. We also demonstrate that MARS has a great interpretability to explain its recommendation results, which is important in many recommendation scenarios.
Quoc Viet Hung Nguyen - One of the best experts on this subject based on the ideXlab platform.
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online User Representation learning across heterogeneous social networks
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019Co-Authors: Weiqing Wang, Hongzhi Yin, Wen Hua, Quoc Viet Hung NguyenAbstract:Accurate User Representation learning has been proven fundamental for many social media applications, including community detection, recommendation, etc. A major challenge lies in that, the available data in a single social network are usually very limited and sparse. In real life, many people are members of several social networks in the same time. Constrained by the features and design of each, any single social platform offers only a partial view of a User from a particular perspective. In this paper, we propose MV-URL, a multi-view User Representation learning model to enhance User modeling by integrating the knowledge from various networks. Different from the traditional network embedding frameworks where either the whole framework is single-network based or each network involved is a homogeneous network, we focus on multiple social networks and each network in our task is a heterogeneous network. It's very challenging to effectively fuse knowledge in this setting as the fusion depends upon not only the varying relatedness of information sources, but also the target application tasks. MV-URL focuses on two tasks: User account linkage (i.e., to predict the missing true User account linkage across social media) and User attribute prediction. Extensive evaluations have been conducted on two real-world collections of linked social networks, and the experimental results show the superiority of MV-URL compared with existing state-of-art embedding methods. It can be learned online, and is trivially parallelizable. These qualities make it suitable for real world applications.
Weiqing Wang - One of the best experts on this subject based on the ideXlab platform.
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online User Representation learning across heterogeneous social networks
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019Co-Authors: Weiqing Wang, Hongzhi Yin, Wen Hua, Quoc Viet Hung NguyenAbstract:Accurate User Representation learning has been proven fundamental for many social media applications, including community detection, recommendation, etc. A major challenge lies in that, the available data in a single social network are usually very limited and sparse. In real life, many people are members of several social networks in the same time. Constrained by the features and design of each, any single social platform offers only a partial view of a User from a particular perspective. In this paper, we propose MV-URL, a multi-view User Representation learning model to enhance User modeling by integrating the knowledge from various networks. Different from the traditional network embedding frameworks where either the whole framework is single-network based or each network involved is a homogeneous network, we focus on multiple social networks and each network in our task is a heterogeneous network. It's very challenging to effectively fuse knowledge in this setting as the fusion depends upon not only the varying relatedness of information sources, but also the target application tasks. MV-URL focuses on two tasks: User account linkage (i.e., to predict the missing true User account linkage across social media) and User attribute prediction. Extensive evaluations have been conducted on two real-world collections of linked social networks, and the experimental results show the superiority of MV-URL compared with existing state-of-art embedding methods. It can be learned online, and is trivially parallelizable. These qualities make it suitable for real world applications.
Jiawei Zhang - One of the best experts on this subject based on the ideXlab platform.
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jscn joint spectral convolutional network for cross domain recommendation
International Conference on Big Data, 2019Co-Authors: Lei Zheng, Jiawei Zhang, Philip S YuAbstract:Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant User Representation with a domain adaptive User mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant User mapping. The domain adaptive User mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9.2% improvement on recall and 36.4% improvement on MAP compared with state-of-the-art methods. Our code is available online 1.1https://github.com/JimLiu96/JSCN
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jscn joint spectral convolutional network for cross domain recommendation
arXiv: Learning, 2019Co-Authors: Lei Zheng, Jiawei Zhang, Philip S YuAbstract:Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a \textbf{J}oint \textbf{S}pectral \textbf{C}onvolutional \textbf{N}etwork (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant User Representation with a domain adaptive User mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant User mapping. The domain adaptive User mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on $24$ Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with $9.2\%$ improvement on recall and $36.4\%$ improvement on MAP compared with state-of-the-art methods. Our code is available online ~\footnote{this https URL}.
