Name
Abstract | ||
|---|---|---|
Social network has become an important way to exchange information, which allows users to post tweets on hot topics and share opinions with others. Typically, tweets on an identical topic are ranked according to either the freshness or the popularity. However, neither of them is ideal, due to the redundancy of tweets' content under the same topic. To address this problem, we intend to diversify the search results in this paper. We propose an effective measure of diversity and a ranking algorithm based on LSH(Locality Sensitive Hash). The proposed solution also works well on the MapReduce framework. Our method can efficiently rank tweets according to their popularity and diversity. We carry out extensive experiments on real-world datasets to verify the effectiveness and efficiency of our methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-08010-9_58 | WEB-AGE INFORMATION MANAGEMENT, WAIM 2014 |
Field | DocType | Volume |
Locality,Social network,Ranking,Computer science,Popularity,Redundancy (engineering),Artificial intelligence,Hash function,Machine learning | Conference | 8485 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zhu | 1 | 82 | 14.36 |
| Yuming Lin | 2 | 37 | 4.76 |
| Ji Cheng | 3 | 0 | 0.34 |
| Xiaoling Wang | 4 | 469 | 72.53 |