Name
Title | ||
|---|---|---|
Distributed Implementation of Latent Rating Pattern Sharing Based Cross-domain Recommender System Approach |
Abstract | ||
|---|---|---|
Latent rating pattern sharing based approaches for cross-domain recommendations can alleviate the data sparsity problem by pulling the knowledge available from other domains and are faster in prediction. However, since the prediction quality depends on number of chosen user and item classes for given data-set, the model training time becomes prohibitively large even for medium size data-sets. In this paper, we propose a MapReduce based distributed implementation of the cross domain recommendation algorithm. Our implementation has the capability to run on modern distributed computing frameworks, such as Hadoop and Twister, that utilize commodity machines. The experimental results show that the training time increases only linearly with user and item classes when compared to the exponential increase in case of its sequential counterpart. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BigData.Congress.2014.77 | BigData Congress |
Keywords | Field | DocType |
distributed processing,recommender systems,MapReduce,commodity machines,cross domain recommendation algorithm,cross-domain recommender system,distributed computing frameworks,latent rating pattern sharing,training time,Big Data,Data sparsity,Flexible Mixture Model,MapReduce,Transfer Learning | Recommender system,Data mining,Exponential function,Computer science,Transfer of learning,Prediction algorithms,Artificial intelligence,Big data,Machine learning,Sparse matrix,Database | Conference |
ISSN | Citations | PageRank |
2379-7703 | 1 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amit Kumar | 1 | 310 | 40.43 |
| Kapur, V. | 2 | 1 | 0.34 |
| Saha, A. | 3 | 1 | 0.68 |
| rajiv gupta | 4 | 4301 | 364.53 |