Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel recommendation method that exploits the intrinsic hierarchical structure of the item space to overcome known shortcomings of current collaborative filtering techniques. A number of experiments on the MovieLens dataset, suggest that our method alleviates the problems caused by the sparsity of the underlying space and the related limitations it imposes on the quality of recommendations. Our tests show that our approach outperforms other state-of-the- art recommending algorithms, having at the same time the advantage of being computationally attractive and easily implementable. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41016-1_6 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Recommender Systems,Collaborative Filtering,Sparsity,Ranking Algorithms,Experiments | Recommender system,Data mining,Learning to rank,Collaborative filtering,Ranking,Computer science,MovieLens,Exploit,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
384 | 1865-0929 | 5 |
PageRank | References | Authors |
0.45 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Athanasios N. Nikolakopoulos | 1 | 59 | 9.02 |
| Marianna Kouneli | 2 | 5 | 0.78 |
| John D. Garofalakis | 3 | 176 | 36.73 |