Exploring an opinion network for taste prediction: An empirical study |
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| Institution: | 1. Department of Physics, University of Fribourg, Chemin du Muse 3, CH-1700 Fribourg, Switzerland;2. Brookhaven National Laboratory, Department of Physics, Upton, New York 11973, USA;1. General Education Center, National Tainan Institute of Nursing, Tai-nan, Taiwan;2. Department of Physics, Chung Yuan Christian University, Chung-li, Taiwan;1. Univ. Orléans, PRISME, EA 4229, F45072 Orléans, France;2. SPCTS, UMR CNRS 7315, ENSCI, Université de Limoges, CEC, 12 Rue Atlantis, 87068 Limoges, France;3. Air Liquide, Centre de Recherche Paris-Saclay, 1 chemin de la porte des Loges, Les Loges-en-Josas, B.P. 126-78354 Jouy-en-Josas Cedex, France;1. Department of Economics, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany;2. Department of Economics, University of Paderborn, Warburger Straße 100, 33098 Paderborn, Germany;3. BiGSEM, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany;1. Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg, Switzerland;2. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, ChongQing, 400714, PR China;1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, PR China;2. Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China;3. Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, PR China;1. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, 212003, China;2. Department of Modern Physics, University of Science and Technology of China, Hefei, 230026, China;3. Systems Science Institute, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | We develop a simple statistical method to find affinity relations in a large opinion network which is represented by a very sparse matrix. These relations allow us to predict missing matrix elements. We test our method on the Eachmovie data of thousands of movies and viewers. We found that significant prediction precision can be achieved and it is rather stable. There is an intrinsic limit to further improve the prediction precision by collecting more data, implying perfect prediction can never obtain via statistical means. |
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