IncRMF: An Incremental Recommendation Algorithm Based on Regularized Matrix Factorization
Authors: 
Lei Sun, Detian Li, Yulu YangAuthors Info & Claims
Lei Sun, Detian Li, Yulu YangAuthors Info & ClaimsPages 203 - 207
Abstract
The rating prediction algorithm based on matrix factorization is one of the research hotspots. When the training data is increased, all data needs to be retrained to improve recommendation results with new changes. When the data is large, the cost of calculation and time will be higher. This paper proposed an incremental recommendation algorithm based on regularized matrix factorization (IncRMF), the algorithm deals with new users and items ratings incrementally, get the updated data of training and ratings prediction by optimizing the previous result, and significantly reduce the amount of calculation in the process of training. Theoretical analysis and experimental results show that this method can guarantee the accuracy of prediction results (the difference of prediction error between IncRMF and normal regularized matrix factorization method not more than 1.36%), while the amount of calculation is significantly reduced.
References
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Index Terms
- IncRMF: An Incremental Recommendation Algorithm Based on Regularized Matrix Factorization
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Published In
October 2018
217 pages
ISBN:9781450365192
DOI:10.1145/3289430
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- Deakin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 October 2018
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BDIOT 2018
BDIOT 2018: 2018 2nd International Conference on Big Data and Internet of Things
October 24 - 26, 2018
Beijing, China
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Overall Acceptance Rate 75 of 136 submissions, 55%
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