Name
Abstract | ||
|---|---|---|
Language Models are state-of-the-art methods in Information Retrieval. Their sound statistical foundation and high effectiveness in several retrieval tasks are key to their current success. In this paper, we explore how to apply these models to deal with the task of computing user or item neighbourhoods in a collaborative filtering scenario. Our experiments showed that this approach is superior to other neighbourhood strategies and also very efficient. Our proposal, in conjunction with a simple neighbourhood-based recommender, showed a great performance compared to state-of-the-art methods (NNCosNgbr and PureSVD) while its computational complexity is low. |
Year | Venue | Field |
|---|---|---|
2016 | ECIR | Recommender system,Data mining,Collaborative filtering,Information retrieval,Computer science,Neighbourhood (mathematics),Artificial intelligence,Machine learning,Language model,Computational complexity theory |
DocType | Citations | PageRank |
Conference | 6 | 0.44 |
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 |