Name
Abstract | ||
|---|---|---|
Social recommender systems aim to alleviate the information overload problem on social network sites. The social network structure is often an important input to these recommender systems. Typically, this structure cannot be inferred directly from declared relationships among users. The goal of our work is to extract an underlying hidden and sparse network which more strongly represents the actual interactions among users. We study how to leverage Twitter activities like micro-blogging and the network structure to find a simple, efficient, but accurate method to infer and expand this hidden network. We measure and compare the performance of several different modeling strategies using a crawled data set from Twitter. Our results reveal that the structural similarity in the network generated by users' retweeting behavior outweighs the other discussed methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2487788.2488095 | WWW (Companion Volume) |
Keywords | Field | DocType |
twitter activity,social network site,social recommender system,actual interaction,recommender system,accurate method,network structure,sparse network,social contact matter,hidden network,social network structure,hidden web,similarity,social networks,trust,recommender systems | Recommender system,Data mining,World Wide Web,Information overload,Social network,Leverage (finance),Computer science,Deep Web,Network structure | Conference |
ISBN | Citations | PageRank |
978-1-4503-2038-2 | 4 | 0.40 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mozhgan Tavakolifard | 1 | 116 | 8.60 |
| Kevin C. Almeroth | 2 | 2551 | 209.40 |
| Jon Atle Gulla | 3 | 591 | 97.84 |