Name
Abstract | ||
|---|---|---|
In this paper, we propose EIGENREC; a simple and versatile Latent Factor framework for Top-N Recommendations, which includes the well-known PureSVD algorithm as a special case. EIGENREC builds a low dimensional model of an inter-item proximity matrix that combines a traditional similarity component, with a scaling operator, designed to regulate the effects of the prior item popularity on the final recommendation list. A comprehensive set of experiments on the MovieLens and the Yahoo datasets, based on widely applied performance metrics suggest that EIGENREC outperforms several state of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, while exhibiting low susceptibility to the problems caused by Sparsity, even its most extreme manifestations - the Cold-start problems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICBK.2017.14 | 2017 IEEE International Conference on Big Knowledge (ICBK) |
Keywords | DocType | ISBN |
Collaborative Filtering,Top-N Recommendation,Latent Factor Methods,PureSVD | Conference | 978-1-5386-3121-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Athanasios N. Nikolakopoulos | 1 | 59 | 9.02 |
| Vassilis Kalantzis | 2 | 0 | 0.34 |
| Efstratios Gallopoulos | 3 | 349 | 105.93 |
| John D. Garofalakis | 4 | 176 | 36.73 |