Name
Abstract | ||
|---|---|---|
Given the diversity of recommendation algorithms, choosing one technique is becoming increasingly difficult. In this paper, we explore methods for combining multiple recommendation approaches. We studied rank aggregation methods that have been proposed for the metasearch task (i.e., fusing the outputs of different search engines) but have never been applied to merge top-N recommender systems. These methods require no training data nor parameter tuning. We analysed two families of methods: voting-based and score-based approaches. These rank aggregation techniques yield significant improvements over state-of-the-art top-N recommenders. In particular, score-based methods yielded good results; however, some voting techniques were also competitive without using score information, which may be unavailable in some recommendation scenarios. The studied methods not only improve the state of the art of recommendation algorithms but they are also simple and efficient. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3077136.3080647 | SIGIR |
Keywords | Field | DocType |
Recommender systems, metasearch, Borda count, Condorcet | Data mining,Borda count,Computer science,Artificial intelligence,Merge (version control),Recommender system,Training set,Metasearch engine,Search engine,Information retrieval,Voting,Algorithm,Machine learning,Condorcet method | Conference |
ISBN | Citations | PageRank |
978-1-4503-5022-8 | 1 | 0.36 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Valcarce | 1 | 54 | 8.51 |
| Javier Parapar | 2 | 188 | 25.91 |
| Alvaro Barreiro | 3 | 226 | 22.42 |