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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Vydáno v:	2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) s. 1048 - 1052
Hlavní autoři:	Hao, Yumeng, Ma, Wenming
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 17.10.2020
Témata:	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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