Abstract
A distributed news event hybrid recommendation approach was proposed to improve efficiency of personalized news recommendation. In this approach, the traditional hierarchical cluster algorithm was modified to find news events, the distance weight of two cluster centers and the maximum distance weight among different clusters were modulated to avoid 'big cluster' in traditional hierarchical cluster. Then a hybrid recommendation algorithm was used to recommend news events, and a users' interest model with multiple event characteristics was introduced into the hybrid recommendation algorithm. At last, this approach was implemented with Spark to deal with big data recommendation. Experimental results on open collections show the effectiveness of our proposed approach.
| Original language | English |
|---|---|
| Pages (from-to) | 721-726 |
| Number of pages | 6 |
| Journal | Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology |
| Volume | 37 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 1 Jul 2017 |
Keywords
- Distribution
- Hierarchical cluster
- Hybrid recommendation
- Spark
- User interest model