Name
Abstract | ||
|---|---|---|
With the rapid growth of web data, people sometime need semantic similar information in order to obtain a clear outline of their interests, so recommendation is needed to provide relevant information to users' queries. In this paper, we propose a method to recommend semantic similar movies and stars to users' queries, styles and stories. The system measures the similarities between movies according to genre and style features extracted from YAGO and IMDB. Experimental results show that the recommendations meet users' interests. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/APWeb.2010.51 | APWeb |
Keywords | Field | DocType |
relevant information,web data,semantic similar movies recommendation,semantic similar information,clear outline,users queries,rapid growth,semantic similar stars recommendation,information filters,internet,yago,semantic similar movie,imdb,query processing,collaboration,semantics,semantic similarity,motion pictures,symmetric matrices,information services,information retrieval,data mining,feature extraction,filtering,search engines | Information system,World Wide Web,Information retrieval,Stars,Computer science,Feature extraction,Database,Semantics,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-4244-6600-9 | 4 | 0.40 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yajie Hu | 1 | 68 | 4.59 |
| Ziqi Wang | 2 | 47 | 4.63 |
| Wei Wu | 3 | 96 | 28.00 |
| Jianzhong Guo | 4 | 19 | 2.00 |
| Ming Zhang | 5 | 1963 | 107.42 |