Name
Abstract | ||
|---|---|---|
A genetic algorithm based solution for the collaborative filtering was proposed.This model was tested on weights computed with different similarity metrics.The performance of different metrics after evolutionary approach was compared. Development of approaches for reducing the prediction error has been an active research field in collaborative filtering recommender systems since the accuracy of the prediction plays a crucial role in user purchase preferences. Unlike the conventional collaborative filtering methods which directly use the computed user-to-user similarity values, this paper presents a genetic algorithm approach for refining them before using in the prediction process. The approach was found to yield promising results according to the statistical analysis performed on a variety numbers of neighbours for various similarity metrics including Pearson's Correlation, Extended Jaccard Coefficient and Vector Cosine Similarity along with a metric that assigns random weights to be used as a benchmark. Results show that the evolutionary approach has significantly reduced the prediction error using the evolved weights and Vector Cosine Similarity has shown the best performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.eswa.2016.05.021 | Expert Syst. Appl. |
Keywords | Field | DocType |
Collaborative filtering,Genetic algorithms,Evaluation,Recommender systems | Recommender system,Data mining,Mean squared prediction error,Collaborative filtering,Cosine similarity,Computer science,Correlation,Artificial intelligence,Jaccard index,Genetic algorithm,Machine learning,Statistical analysis | Journal |
Volume | Issue | ISSN |
61 | C | 0957-4174 |
Citations | PageRank | References |
17 | 0.70 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ar, Y. | 1 | 31 | 1.37 |
| Erkan Bostanci | 2 | 65 | 9.18 |