Name
Abstract | ||
|---|---|---|
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users' tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt spreading activation theory to assign user interest score to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of users' explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2567948.2577353 | WWW (Companion Volume) |
Keywords | Field | DocType |
common challenge,hierarchical interest graph,richer user interest,implicit interest category,structured background knowledge,user interest score,wikipedia category graph,background knowledge source,user interest,ground potential user interest,social semantic web,wikipedia,personalization | Data mining,Graph,World Wide Web,Social media,Spreading activation,Computer science,Microblogging,Granularity,Social Semantic Web,Hierarchy,Semantics,Personalization | Conference |
Citations | PageRank | References |
1 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pavan Kapanipathi | 1 | 125 | 10.66 |
| Prateek Jain | 2 | 1 | 0.73 |
| Chitra Venkataramani | 3 | 1 | 0.40 |
| Amit P. Sheth | 4 | 10950 | 1885.56 |