Normalized Matrix Factorization with Implicit FeedBack and Baseline Predictor

The traditional matrix factorization algorithm has the problems of cold start, data sparsity and high predictive time complexity, which is not perfect in most recommendation systems. Especially in the sparse matrix problem, the recommendation accuracy cannot be guaranteed. This paper will be based o...

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Veröffentlicht in:2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) S. 1048 - 1052
Hauptverfasser: Hao, Yumeng, Ma, Wenming
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.10.2020
Schlagworte:
Batch Norm
cold startup
computational complexity
Data models
data sparse problems
high predictive time complexity
implicit feedback
information filtering
matrix decomposition
Matrix factorization
matrix factorization algorithm modeling
mean square error methods
Movielens-1M experiment
Normalized
normalized matrix factorization
optimisation
Optimization
optimization algorithm
Prediction algorithms
Predictive models
recommender systems
Sparse data
sparse matrices
sparse matrix problem
Training
training normalization processing parameters
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Zusammenfassung:The traditional matrix factorization algorithm has the problems of cold start, data sparsity and high predictive time complexity, which is not perfect in most recommendation systems. Especially in the sparse matrix problem, the recommendation accuracy cannot be guaranteed. This paper will be based on the matrix factorization algorithm modeling and create optimization algorithm, this method will be graded dataset preprocessing, the user and item's score matrix by embedding process at the same time and introducing BatchNorm sparse matrix algorithm for training normalization processing parameters do to speed up the convergence speed, increase the stability of the training, respectively add item bias that more able to show the user's true score, then the user matrix to join implicit feedback, the final score projections for the user. The optimization algorithm performs well in solving cold startup and data sparse problems of matrix factorization. The Results of the Movielens-1M experiment show that it has great advantages over traditional matrix factorization algorithms in terms of prediction accuracy, root mean square error and square absolute error.
DOI:10.1109/CISP-BMEI51763.2020.9263526