| Experimental Setup | We set the damping factor ,u to 0.15 following the standard PageRank paradigm. |
| Results | PageRank 0.493 0.481 0.509 0.536 0.604 PersRank 0.501 0.542 0.558 0.560 0.611 DivRank 0.487 0.505 0.518 0.523 0.585 CoRank 0.519 0.546 0.550 0.585 0.617 |
| Results | PageRank 0.557 0.549 0.623 0.559 0.588 PersRank 0.571 0.595 0.655 0.613 0.601 DivRank 0.538 0.591 0.594 0.547 0.589 CoRank 0.637 0.644 0.715 0.643 0.628 |
| Results | Tables 3 and 4 show how the performance of our co-ranking algorithm varies when considering only tweet popularity using the standard PageRank algorithm, personalization (PersRank), and diversity (DivRank). |
| Tweet Recommendation Framework | Popularity We rank the tweet network following the PageRank paradigm (Erin and Page, 1998). |
| Tweet Recommendation Framework | Personalization The standard PageRank algorithm performs a random walk, starting from any node, then randomly selects a link from that node to follow considering the weighted matrix M, or jumps to a random node with equal probability. |
| Tweet Recommendation Framework | In contrast to PageRank , DivRank assumes that the transition probabilities change over time. |