Name
Abstract | ||
|---|---|---|
Recommender system emerges as a technology addressing "information overload" problem. Collaborative Filtering (CF) is successful and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper is with an eye to missing data imputation strategies in nearest-neighbor CF. We propose two novel effective CF approaches based on missing data imputation, which utilizes user’s demographic information before conducting CF process. In the experiments, user’s age range and occupation information are employed in the imputation stage. The results show that the proposed approaches effectively smooth the sparsity of rating data, and perform better prediction than traditional widely-used CF algorithms. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NCM.2009.128 | NCM |
Keywords | Field | DocType |
data analysis,nearest neighbor,digital library,recommender system,collaboration,e commerce,groupware,recommender systems,collaborative filtering,data mining,missing data,internet,prediction algorithms,information overload | Recommender system,Data mining,Information overload,Collaborative filtering,Collaborative software,Computer science,Artificial intelligence,Digital library,Imputation (statistics),Machine learning,Missing data imputation,The Internet | Conference |
Volume | Issue | Citations |
null | null | 5 |
PageRank | References | Authors |
0.47 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Xia | 1 | 28 | 14.30 |
| Liang He | 2 | 34 | 6.02 |
| Junzhong Gu | 3 | 113 | 34.92 |
| He Keqin | 4 | 5 | 0.81 |