Novel Distributed Multimedia Recommendation Systems Using Personalized Information
In this paper, we propose a novel distributed multimedia recommendation system (DMRS) to address personalized preference by use of the matrix-sketching approach for dimensionality reduction and local information updates. Conventional recommendation systems can hardly address scalability, privacy, an...
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| Vydané v: | IEEE transactions on broadcasting s. 1 - 12 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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2025
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| Abstract | In this paper, we propose a novel distributed multimedia recommendation system (DMRS) to address personalized preference by use of the matrix-sketching approach for dimensionality reduction and local information updates. Conventional recommendation systems can hardly address scalability, privacy, and robustness, all of which are very important in practice. To combat the aforementioned challenges, we propose to incorporate local information updates based on local private information at the client side to protect privacy by restricting users' data from the server and utilize the matrix-sketching scheme to further reduce the dimensionality of the global user-item interaction data so that the personalized (distributed) recommendations can be made by users' devices in local. To evaluate the system performance, we define a new robustness measure, namely ϵ-robustness, which quantifies the performance consistency of the recommendation system and involves both sketching errors and local rating updates. Furthermore, we introduce a novel randomized matrix-factorization algorithm to achieve the desired robustness while still maintaining the interaction-data fidelity in terms of normalized root-mean-square error (NRMSE). Our experimental results on both simulated and real-world data demonstrate the effectiveness of our proposed novel DMRS in attaining a good balance between the interaction-data fidelity and the system robustness subject to the privacy protection. |
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| AbstractList | In this paper, we propose a novel distributed multimedia recommendation system (DMRS) to address personalized preference by use of the matrix-sketching approach for dimensionality reduction and local information updates. Conventional recommendation systems can hardly address scalability, privacy, and robustness, all of which are very important in practice. To combat the aforementioned challenges, we propose to incorporate local information updates based on local private information at the client side to protect privacy by restricting users' data from the server and utilize the matrix-sketching scheme to further reduce the dimensionality of the global user-item interaction data so that the personalized (distributed) recommendations can be made by users' devices in local. To evaluate the system performance, we define a new robustness measure, namely ϵ-robustness, which quantifies the performance consistency of the recommendation system and involves both sketching errors and local rating updates. Furthermore, we introduce a novel randomized matrix-factorization algorithm to achieve the desired robustness while still maintaining the interaction-data fidelity in terms of normalized root-mean-square error (NRMSE). Our experimental results on both simulated and real-world data demonstrate the effectiveness of our proposed novel DMRS in attaining a good balance between the interaction-data fidelity and the system robustness subject to the privacy protection. |
| Author | Wu, Hsiao-Chun Huang, Scott Chih-Hao Chang, Shih Yu Wu, Yiyan Yan, Kun |
| Author_xml | – sequence: 1 givenname: Shih Yu orcidid: 0000-0002-3576-0021 surname: Chang fullname: Chang, Shih Yu email: shihyu.chang@sjsu.edu organization: Department of Applied Data Science, San Jose State University, San Jose, CA, USA – sequence: 2 givenname: Hsiao-Chun orcidid: 0000-0002-0178-1246 surname: Wu fullname: Wu, Hsiao-Chun email: eceprofessorwu@gmail.com organization: School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, USA – sequence: 3 givenname: Kun orcidid: 0000-0002-2811-3758 surname: Yan fullname: Yan, Kun email: kyan5702@gmail.com organization: Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing and the Department of Information and Telecommunication, Guilin University of Electronic Technology, Guilin, Guangxi, China – sequence: 4 givenname: Scott Chih-Hao orcidid: 0000-0001-9896-1325 surname: Huang fullname: Huang, Scott Chih-Hao email: chhuang@ee.nthu.edu.tw organization: Department of Electrical Engineering, National Tsinghua University, Hsinchu, Taiwan – sequence: 5 givenname: Yiyan orcidid: 0000-0001-8890-5389 surname: Wu fullname: Wu, Yiyan email: yiyan.wu@ieee.org organization: Department of Electrical and Computer Engineering, Western University, London, ON, Canada |
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| Snippet | In this paper, we propose a novel distributed multimedia recommendation system (DMRS) to address personalized preference by use of the matrix-sketching... |
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| SubjectTerms | Collaborative filtering Digital multimedia broadcasting Dimensionality reduction Distributed databases Distributed multimedia recommendation system (DMRS) Electronic mail matrix sketching Multimedia systems normalized root-mean-square error (NRMSE) Privacy randomized matrix-factorization algorithm Recommender systems Robustness Servers ϵ-robustness |
| Title | Novel Distributed Multimedia Recommendation Systems Using Personalized Information |
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