[Retracted] Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Algorithm 1

Collaborative filtering ad recommendation algorithm with labels.

	Input: Target Query page (i = 1, 2, ..., m), Query page set Q(|Q | = r), ad keyword set K (|K| = n), ad set A, CTR set C, number of neighbors N.
Output the best recommended ad set for the target Query page A
Step 1	For each Query page in the set, 1 ≤ j ≤ Q, j ≠ 1, the loop performs the following operations.
Step 2	Calculate the co-hit similarity between Query pages .
Step 3	Calculate the similarity of co-labeling between Query pages .
Step 4	Calculate the similarity of co-contained relationships between Query pages .
Step 5	Calculate the combined similarity between Query pages .
Step 6	Sort the remaining objects in the set, except the target Query page , from largest to smallest, according to .
Step 7	select the top n query pages in the set as the nearest neighborhood of the target query page .
Step 8	Select the top N ads with the highest predicted click-through rate in the set A’ as the TOP-N best recommended ad set A. The key time overhead of the ADR-CF_T algorithm is the similarity calculation between Query pages, and the time overhead for calculating the co-hit similarity SimQA between Query pages is the same as the traditional CF algorithm [32].