Name
Abstract | ||
|---|---|---|
In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items; the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1587/transinf.E97.D.622 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
recommender systems, user's preference, interactive evolutionary computation, content-based filtering, data grouping | Recommender system,Interactive evolutionary computation,Information retrieval,Computer science | Journal |
Volume | Issue | ISSN |
E97D | 3 | 0916-8532 |
Citations | PageRank | References |
1 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun-Tae Kim | 1 | 13 | 7.80 |
| Jinung An | 2 | 115 | 20.43 |
| Chang Wook Ahn | 3 | 759 | 60.88 |