Name
Abstract | ||
|---|---|---|
Due the success of emerging Web 2.0, and different social network Web sites such as Amazon and movie lens, recommender systems are creating unprecedented opportunities to help people browsing the web when looking for relevant information, and making choices. Generally, these recommender systems are classified in three categories: content based, collaborative filtering, and hybrid based recommendation systems. Usually, these systems employ standard recommendation methods such as artificial neural networks, nearest neighbor, or Bayesian networks. However, these approaches are limited compared to methods based on web applications, such as social networks or semantic web. In this paper, we propose a novel approach for recommendation systems called semantic social recommendation systems that enhance the analysis of social networks exploiting the power of semantic social network analysis. Experiments on real-world data from Amazon examine the quality of our recommendation method as well as the performance of our recommendation algorithms. |
Year | Venue | Keywords |
|---|---|---|
2014 | CoRR | social network,recommender system,semantic web |
Field | DocType | Volume |
Recommender system,Data mining,World Wide Web,Collaborative filtering,Social network,Semantic Web Stack,Information retrieval,Computer science,Semantic Web,Social Semantic Web,Web application,Semantic social network | Journal | abs/1407.3392 |
Citations | PageRank | References |
3 | 0.39 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| khaled sellami | 1 | 3 | 0.73 |
| mohamed ahmednacer | 2 | 3 | 0.39 |
| pierre f tiako | 3 | 3 | 0.39 |
| Rachid Chelouah | 4 | 405 | 37.20 |
| Hubert Kadima | 5 | 14 | 4.10 |