Name
Title | ||
|---|---|---|
Context Aware Matrix Factorization for Event Recommendation in Event-Based Social Networks |
Abstract | ||
|---|---|---|
Event-based Social Networks(EBSNs) which combine online interactions and offline events among users have experienced increased popularity and rapid growth recently. In EBSNs, event recommendation is significant for users due to the extremely large amount of events. However, the event recommendation problem is rather challenging because it faces a serious cold-start problem: Events have short life time and new events are registered by only a few users. What's more, there are only implicit feedback information. Existing approaches like collaborative filtering methods are not suitable for this scenario. In this paper, we propose a Context Aware Matrix Factorization model called AlphaMF to tackle with the problem. Specifically, AlphaMF is a unified model that combines the Matrix Factorization model which models implicit feedbacks with the Linear contextual features model which models explicit contextual features. Extensive experiments on a large real-world EBSN dataset demonstrate that the AlphaMF model significantly outperforms state-of-the-art methods by 11%. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WI.2016.0043 | 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) |
Keywords | Field | DocType |
event recommendation,context aware,matrix factorization,Recommender Systems,Event-based Social Networks | Data mining,Social network,Computer science,Popularity,Context model,Artificial intelligence,Unified Model,Life time,Recommender system,Collaborative filtering,Information retrieval,Matrix decomposition,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-4471-9 | 0 | 0.34 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulong Gu | 1 | 8 | 3.85 |
| Jiaxing Song | 2 | 50 | 9.62 |
| Weidong Liu | 3 | 93 | 17.66 |
| Lixin Zou | 4 | 39 | 4.81 |
| Yuan Yao | 5 | 8 | 2.51 |