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
Online-Zugang:	Volltext
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