Learning from user interactions for recommending content in social media
| Authors |
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| Publication date | 2014 |
| Host editors |
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| Book title | Advances in Information Retrieval |
| Book subtitle | 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014: proceedings |
| ISBN |
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| ISBN (electronic) |
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| Series | Lecture Notes in Computer Science |
| Event | 36th European Conference on Information Retrieval (ECIR'14) |
| Pages (from-to) | 598-604 |
| Publisher | Cham: Springer |
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| Abstract | We study the problem of recommending hyperlinks to users in social media. We start with a candidate set of links posted by a user's social circle (e.g., friends, followers) and rank these links using a combination of (i) a user interaction model, and (ii) the similarity of a user profile and a candidate link. Experiments on two datasets demonstrate that our method is robust and, on average, outperforms, a strong chronological baseline. |
| Document type | Conference contribution |
| Language | English |
| Published at |
https://doi.org/10.1007/978-3-319-06028-6_63
(Final published version)
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