Name
Abstract | ||
|---|---|---|
In many situations, related to some types of systems or organizations' tasks, it is necessary to identify people with similar profiles. In the case of a collaborative recommender system, items to be recommended are those associated to similar users. Another example, in the academic environment, is to identify new members to be part of a research group (people with similar profiles). This task of identifying people with similar profiles can be time-consuming. In this sense, this work considers that scientific papers written by people can be used to identify users with similar profiles. Considering this assumption, we have done some experiments to identify which parts of papers, which type of indexes (terms or concepts) and which type of similarity functions (Jaccard or a Fuzzy function) are more suitable to identify similar people. The paper presents the results of some experiments and some application scenarios considering academic environments. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-12436-5_17 | Lecture Notes in Business Information Processing |
Keywords | Field | DocType |
Knowledge Management,People Profile,User Profile Similarity,Collaborative Recommender Systems | Recommender system,Data mining,World Wide Web,Information retrieval,Computer science,Fuzzy logic,Jaccard index | Conference |
Volume | ISSN | Citations |
45 | 1865-1348 | 1 |
PageRank | References | Authors |
0.35 | 19 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanley Loh | 1 | 83 | 8.49 |
| Fabiana Lorenzi | 2 | 83 | 7.60 |
| Roger L. Granada | 3 | 20 | 7.33 |
| Daniel Lichtnow | 4 | 15 | 5.61 |
| Leandro Krug Wives | 5 | 238 | 25.10 |
| José Palazzo Moreira de Oliveira | 6 | 189 | 27.74 |