Name
Abstract | ||
|---|---|---|
Neighborhood based collaborative filtering is a popular approach in recommendation systems. In this paper we propose to apply evolutionary computation to reduce the size of the model used for the recommendation. We formulate the problem of constructing the set of neighbors as an optimization problem that we tackle by stochastic local search. The results we present show that our approach produces a set of global neighbors made up of less than 16% of the entire set of users, thus decreases the size of the model by 84%. Furthermore, this reduction leads to a slight increase of the accuracy of a state of the art clustering based approach, without impacting the coverage. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2245276.2245322 | SAC |
Keywords | Field | DocType |
slight increase,popular approach,evolutionary computation,present show,recommendation system,entire set,global neighbor,optimization problem,stochastic search,stochastic local search,global neighbors selection,collaborative filtering,iterated local search,recommender system,evolutionary computing | Recommender system,Data mining,Collaborative filtering,Computer science,Evolutionary computation,Artificial intelligence,Local search (optimization),Cluster analysis,Optimization problem,Machine learning,Iterated local search | Conference |
Citations | PageRank | References |
5 | 0.42 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amine Boumaza | 1 | 48 | 6.46 |
| Armelle Brun | 2 | 138 | 21.49 |