Name
Abstract | ||
|---|---|---|
The topic of recommender systems is rapidly gaining interest in the user-behaviour modeling research domain. Over the years, various recommender algorithms based on different mathematical models have been introduced in the literature. Researchers interested in proposing a new recommender model or modifying an existing algorithm should take into account a variety of key performance indicators, such as execution time, recall and precision. Till date and to the best of our knowledge, no general cross-validation scheme to evaluate the performance of recommender algorithms has been developed. To fill this gap we propose an extension of conventional cross-validation. Besides splitting the initial data into training and test subsets, we also split the attribute description of the dataset into a hidden and visible part. We then discuss how such a splitting scheme can be applied in practice. Empirical validation is performed on traditional user-based and item-based recommender algorithms which were applied to the MovieLens dataset. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-27387-2_25 | PerMin |
Keywords | Field | DocType |
various recommender,movielens dataset,general cross-validation scheme,conventional cross-validation,recommender algorithm,recommender system,new recommender model,key performance indicator,new cross-validation technique,splitting scheme,item-based recommender algorithm,recommender systems | Recommender system,Data mining,Performance indicator,Computer science,Precision and recall,MovieLens,Execution time,Artificial intelligence,Mathematical model,Cross-validation,Machine learning | Conference |
Citations | PageRank | References |
10 | 0.53 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitry I. Ignatov | 1 | 239 | 29.53 |
| Jonas Poelmans | 2 | 277 | 19.28 |
| Guido Dedene | 3 | 925 | 83.39 |
| Stijn Viaene | 4 | 722 | 60.17 |