| 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 |