[Retracted] Language Intelligence Development of English Multimedia Teaching considering Collaborative Filtering Topic Search Algorithm

Algorithm 2

The running process of the CTR model.

	Input: the user’s regularization coefficient and the recommended item’s regularization coefficient .
	Output: the approximation matrix X of the matrix R.
①	For each user i, first extract the corresponding feature vector, namely, ;
②	For each recommended item in text form j;
(i)	Use the LDA model described in Algorithm 1 to get the topic distribution .
(ii)	Get the potential variance of recommended items , to satisfy the distribution 。
(iii)	Get the feature vector of the recommended item .
	Is .
③	For each scoring point (i, j), the corresponding prediction score is obtained, as shown in the following formula: