Name
Abstract | ||
|---|---|---|
Probabilistic modelling of recommender systems naturally introduces the concept of prior probability into the recommendation task. Relevance-Based Language Models, a principled probabilistic query expansion technique in Information Retrieval, has been recently adapted to the item recommendation task with success. In this paper, we study the effect of the item and user prior probabilities under that framework. We adapt two priors from the document retrieval field and then we propose other two new probabilistic priors. Evidence gathered from experimentation indicates that a linear prior for the neighbour and a probabilistic prior based on Dirichlet smoothing for the items improve the quality of the item recommendation ranking. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2792838.2799677 | Conference on Recommender Systems |
Field | DocType | Citations |
Recommender system,Data mining,Collaborative filtering,Query expansion,Ranking,Computer science,Artificial intelligence,Probabilistic logic,Document retrieval,Prior probability,Language model,Machine learning | Conference | 5 |
PageRank | References | Authors |
0.42 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Valcarce | 1 | 54 | 8.51 |
| Javier Parapar | 2 | 188 | 25.91 |
| Alvaro Barreiro | 3 | 226 | 22.42 |