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| Distributed Singular Value Decomposition Recommendation Algorithm Based on LU Decomposition and Alternating Least Square |
| LI Lin1 , WANG Peipei1 , GU Peng1 , XIE Qing1 |
| 1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070 |
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Abstract Aiming at the problems of high time complexity and long running time of the current distributed potential factor recommendation algorithm, a distributed singular value decomposition recommendation algorithm based on LU decomposition and alternating least square(ALS) is proposed. Based on the characteristics of ALS for distributed solution of objective function, a grid-like distributed granularity segmentation strategy is proposed to obtain independent and unrelated feature vectors. When the characteristic matrix is updated, LU decomposition is adopted to solve the inverse matrix to speed up the operation of the algorithm. The experiment on Tencent Weibo dataset in KDD CUP 2012 Track1 indicates that the recommendation speed and efficiency of the proposed algorithm is significantly improved on the premise of ensuring a certain recommendation accuracy.
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Received: 20 August 2019
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| Fund:National Social Science Foundation of China(No.15BGL048), National Natural Science Foundation of China(No.61602353), Science and Technology Support Project of Hubei Province(No.2015BAA072), Fundamental Research Funds for the Central Universities(No.WUT:2017II39GX,2016-YS-068) |
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Corresponding Authors:
LI Lin, Ph.D., professor. Her research interests include information retrieval, recommender system, machine learning and data mining.
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| About author:: WANG Peipei, Ph.D. candidate. Her research interests include natural language processing and recommender system.GU Peng, master. His research interests include data mining, group recommendation and information retrieval.XIE Qing, Ph.D., associate professor. His research interests include stream mining, pa-ttern analysis, knowledge service and reco-mmender system. |
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2020, Vol. 33 
