Name
Abstract | ||
|---|---|---|
Online social networks have become an essential part of our daily life, and an increasing number of users are using multiple online social networks simultaneously. We hypothesize that the integration of data from multiple social networks could boost the performance of recommender systems. In our study, we perform cross-social network collaborative recommendation and show that fusing multi-source data enables us to achieve higher recommendation performance as compared to various single-source baselines. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2786451.2786504 | WebSci |
Field | DocType | Citations |
Recommender system,World Wide Web,Social media,Social network,Computer science,Baseline (configuration management),Polarization (politics) | Conference | 8 |
PageRank | References | Authors |
0.53 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleksandr Farseev | 1 | 92 | 7.48 |
| Denis Kotkov | 2 | 50 | 4.74 |
| Alexander Semenov | 3 | 80 | 19.40 |
| Jari Veijalainen | 4 | 388 | 93.08 |
| Tat-Seng Chua | 5 | 11749 | 653.09 |