Name
Abstract | ||
|---|---|---|
Collaborative filtering is one of most important technologies in the field of recommender systems, the process of making predictions about user preferences for products or services by learning known user-item relationships. In this paper, slope one and item-based nearest neighbor collaborative filtering algorithms are analyzed on the Movie Lens dataset. In order to obtain better accuracy and rationality, a new combined approach is proposed that takes advantages of slope one and item-based nearest neighbor model. In addition, simple gradient descent and bias effects are used further to improve performance. Finally, some experiments are implemented on the dataset, and the experimental results show that the proposed final solution achieves great improvement of prediction accuracy when compared to the method of using slope one or item-based nearest neighbor model alone. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/INCoS.2013.46 | INCoS |
Keywords | Field | DocType |
user-item relationship learning,great improvement,item-based nearest neighbor,prediction accuracy,collaborative filtering,item-based collaborative filtering,slope one algorithm,learning (artificial intelligence),movie lens dataset,recommender systems,slope one,combined predictor,important technology,nearest neighbor model,item-based nearest neighbor collaborative filtering algorithms,bias effects,nearest neighbor collaborative,better accuracy,bias effect,gradient descent,proposed final solution,learning artificial intelligence | Recommender system,k-nearest neighbors algorithm,Data mining,Gradient descent,Slope One,Collaborative filtering,Computer science,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghuo Wu | 1 | 0 | 0.34 |
| Jun Zheng | 2 | 18 | 4.87 |
| Su Wang | 3 | 1 | 2.09 |
| Hongfeng Feng | 4 | 0 | 0.34 |