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IEEE/ICACT20230130 Slide.10        [Big Slide]       [YouTube]	Oral Presentation
	Because the ALS algorithm generates a full matrix, it is always able to make recommendations. There are two k dimensional vectors which are referred to as ¡°factor¡±. 𝑥_𝑢 is k dimensional vectors summarizing¡¯s every user u. 𝑦_𝑖 is k dimensional vectors summarizing¡¯s every item i. Use stochastic gradient descent to minimize the squared error between the predicted rating the actual rating, Here, 𝜆 is the regularization factor, which is used to address the over fitting issue, is referred as weighted- 𝜆-regularization. The default value of 𝜆 is 1.

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