Name
Abstract | ||
|---|---|---|
Recommender systems aim to facilitate World Wide Web users against information and product overloading. They are usually intermediate programs that try to predict users' preferences and items of their interest. In this paper, we present a hybrid framework that uses open source information such as web logs in combination with social network analysis and data mining, to extract useful information about users browsing patterns and construct a recommendation engine. A case study based on real data from an organization of 250 employees is presented and a system prototype is constructed based on the results. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/EISIC.2011.40 | EISIC |
Keywords | Field | DocType |
hybrid framework,data mining,open source information,recommender system,intermediate program,world wide web user,web-page recommender system,product overloading,case study,useful information,social network,association rules,recommender systems,internet,web pages,information retrieval | Recommender system,World Wide Web,Social network,Web page,Computer science,Social network analysis,Association rule learning,The Internet | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vasileios Anastopoulos | 1 | 1 | 1.38 |
| Panagiotis Karampelas | 2 | 34 | 15.16 |
| Panagiotis Kalagiakos | 3 | 3 | 3.74 |
| Reda Alhajj | 4 | 1919 | 205.67 |