Name
Abstract | ||
|---|---|---|
The recommendation framework based on precedence mining as outlined in [3] is limited to personal recommendation and cannot be trivially extended for group recommendation scenario. In this paper, we extend the precedence mining model for group recommendation by proposing a novel way of defining a virtual user by taking <Literal>transitive precedence relation</Literal> into account. We obtained experimental results for different combinations of parameter settings and for different group-sizes on <Literal>MovieLens</Literal> data-set based on our virtual-user model. We show that our framework has better performance in terms of <Literal>precision and recall</Literal> when compared with other methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-03680-9_43 | Australasian Conference on Artificial Intelligence |
Field | DocType | Citations |
Recommender system,Data mining,Information retrieval,Computer science,MovieLens,Precision and recall,Virtual user,Transitive relation | Conference | 3 |
PageRank | References | Authors |
0.38 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Venkateswara Rao Kagita | 1 | 59 | 8.13 |
| Arun K. Pujari | 2 | 420 | 48.20 |
| Vineet Padmanabhan | 3 | 216 | 25.90 |