| | Input: domain knowledge graph KG, users set U, G, document set docs, < document name, instance, similarity > triple list |
| | Output: recommendation document set |
| (0) | Processing the document set docs through LSTM |
| (1) | for i = 1 to n do |
| (2) | computing user interest similarity sim(q,d), and obtain a similar interest user set U2 |
| (3) | for each user in users associated with the user’s interests in KG do |
| (4) | topic recommendations based on interest graph to obtain document set L1 and obtain a similar interest user set U1 |
| (5) | end |
| (6) | for each individual ins in <document, instance, similarity > triplet list do |
| (7) | content recommendations based on semantic annotation to obtain document set L2 |
| (8) | end |
| (9) | for each document in docs the user u has not acted on do |
| (10) | for each user in users of the intersection of U1 and U2 having acted on document j do |
| (11) | predicting user document behavior evaluation P(u,j) |
| (12) | for each user in G having acted on document j do |
| (13) | computing the user group and the document consensus function F(G,j) to obtain document set L3 |
| (14) | end |
| (15) | end |
| (16) | end |
| (17) | do |
| (18) | the intersection of L1, L2 and L3, then sort by Top-k |
| (19) | return recommendation document set |
| (20) | end |
