Name
Abstract | ||
|---|---|---|
In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2245276.2245354 | SAC |
Keywords | Field | DocType |
semantic similarity,movie recommendation,semantic version,facebook application,classical vector space model,linked data,social knowledge base,vector space model,knowledge base,semantic web,ontologies | Semantic similarity,Semantic technology,Information retrieval,Semantic Web Stack,Computer science,Explicit semantic analysis,Semantic analytics,Semantic grid,Social Semantic Web,Semantic computing | Conference |
Citations | PageRank | References |
1 | 0.35 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Mirizzi | 1 | 330 | 16.59 |
| Tommaso Di Noia | 2 | 1857 | 152.07 |
| Eugenio Di Sciascio | 3 | 1733 | 147.71 |
| Azzurra Ragone | 4 | 511 | 40.86 |