Article Structure
Abstract
As one of the most popular micro—blogging services, Twitter attracts millions of users, producing millions of tweets daily.
Introduction
Online micro-blogging services have revolutionized the way people discover, share, and distribute information.
Related Work
Tweet Search Given the large amount of tweets being posted daily, ranking strategies have become extremely important for retrieving information quickly.
Tweet Recommendation Framework
Our method operates over a heterogeneous network that connects three graphs representing the tweets, their authors and the relationships between them.
Experimental Setup
(13)
Results
Our results are summarized in Tables 1 and 2.
Conclusions
We presented a co-ranking framework for a tweet recommendation system that takes popularity, personalization and diversity into account.
Topics
PageRank
- Popularity We rank the tweet network following the PageRank paradigm (Erin and Page, 1998).Page 3, “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.Page 4, “Tweet Recommendation Framework”
- In contrast to PageRank , DivRank assumes that the transition probabilities change over time.Page 4, “Tweet Recommendation Framework”
- We rank the author network using PageRank analogously to equation (1).Page 5, “Tweet Recommendation Framework”
- So far we have described how we rank the network of tweets GM and their authors GU independently following the PageRank paradigm.Page 5, “Tweet Recommendation Framework”
- This amounts to separately ranking tweets and authors by PageRank .Page 5, “Tweet Recommendation Framework”
- We set the damping factor ,u to 0.15 following the standard PageRank paradigm.Page 6, “Experimental Setup”
- 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 0.617Page 8, “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 0.628Page 8, “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).Page 8, “Results”
- The PageRank algorithm on its own makes good recommendations, while incorporating personalization improves the performance substantially, which indicates that individual users show preferences to specific topics or other users.Page 8, “Results”
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topic distribution
- Given n topics, we obtain a topic distribution matrix D using Latent Dirichlet Allocation (Blei et al., 2003).Page 4, “Tweet Recommendation Framework”
- Consider a user with a topic preference vector t and topic distribution matrix D. We calculate the response probability 1' for all tweets for this user as:Page 4, “Tweet Recommendation Framework”
- ,rw]1xw, where w<|VM| for a given user and the topic distribution matrix D, our task is estimate the topic preference vector t. We do this using maximum-likelihoodPage 4, “Tweet Recommendation Framework”
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