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| Content Provider | ACM Digital Library |
|---|---|
| Author | Schmidt-Thieme, Lars Drumond, Lucas Nejdl, Wolfgang Gantner, Zeno Diaz-Aviles, Ernesto |
| Abstract | Users engaged in the Social Web increasingly rely upon continuous streams of Twitter messages (tweets) for real-time access to information and fresh knowledge about current affairs. However, given the deluge of tweets, it is a challenge for individuals to find relevant and appropriately ranked information. We propose to address this knowledge management problem by going beyond the general perspective of information finding in Twitter, that asks: "What is happening right now?", towards an individual user perspective, and ask: "What is interesting to me right now?" In this paper, we consider collaborative filtering as an online ranking problem and present RMFO, a method that creates, in real-time, user-specific rankings for a set of tweets based on individual preferences that are inferred from the user's past system interactions. Experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend and Weighted Regularized Matrix Factorization (WRMF), a highly competitive state-of-the-art Collaborative Filtering technique, demonstrating the efficacy of our approach. |
| Starting Page | 1592 |
| Ending Page | 1596 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450311564 |
| DOI | 10.1145/2396761.2398479 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-10-29 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Collaborative filtering Online ranking Twitter |
| Content Type | Text |
| Resource Type | Article |
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