| Input: User A and Knowledge K |
| Output: degree of preference of User A to retweet Knowledge K |
| Step 1. |
| initiate User A, Knowledge K; |
| // initiate User A, obtaining User A’s historical behavioral information (the number of releasing messages (num), the number of |
| retweeting messages (num 1)) and the persons who release messages in Wechat K, the nearest propagators and the original |
| content ect.; |
| Step 2. |
| make judgment on the type of User A and the type of events |
| // make judgment on the type of User A (User_type) and the type of events involved in Wechat (Infor_type) |
| Step 3. |
| Based on the type of the user and the type of events we can judge whether it satisfies the end condition; |
| // If User_type = 0 ∨ User_type = 1, we judge the user will not retweet the message and the algorithm ends; If User_type = 2 and |
| Infor_type = 1, we judge the user will retweet the message and the algorithm ends; If User_type = 2 and Infor_type = 0, then we |
| need further judgment and enter the fourth step; |
| Step 4. |
| If it does not satisfy the end condition, we need to select the best feature item combination to further evaluate; |
| // select the best feature item combination and attribute it to the decision preference set |
| Step 5. |
| calculate the characteristic value of the feature item selected |
| // obtain the value of the feature item |
| Step 6. |
| obtain the degree of preference of User A to retweet Knowledge K |
| // Calculate the degree of retweeting preference of the user |
