Name
Title | ||
|---|---|---|
On using category experts for improving the performance and accuracy in recommender systems |
Abstract | ||
|---|---|---|
A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2396761.2398639 | CIKM |
Keywords | Field | DocType |
real-world data,target user,category preference,existing method,recommender system,recommendation method,extensive experiment,category expert,collaborative filtering | Recommender system,Data mining,Collaborative filtering,Information retrieval,Computer science | Conference |
Citations | PageRank | References |
8 | 0.54 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Won-Seok Hwang | 1 | 91 | 6.87 |
| Jong-Ho Lee | 2 | 193 | 35.44 |
| Sang-Wook Kim | 3 | 792 | 152.77 |
| Minsoo Lee | 4 | 315 | 31.33 |