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 |