2014 WhotoFollowandWhyLinkPrediction
- (Barbieri et al., 2014) ⇒ Nicola Barbieri, Francesco Bonchi, and Giuseppe Manco. (2014). “Who to Follow and Why: Link Prediction with Explanations.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623733
Subject Headings:
Notes
Cited By
- http://scholar.google.com/scholar?q=%222014%22+Who+to+Follow+and+Why%3A+Link+Prediction+with+Explanations
- http://dl.acm.org/citation.cfm?id=2623330.2623733&preflayout=flat#citedby
Quotes
Author Keywords
Abstract
User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty.
In this paper we study link prediction with explanations for user recommendation in social networks. For this problem we propose WTFW (" Who to Follow and Why "), a stochastic topic model for link prediction over directed and nodes-attributed graphs. Our model not only predicts links, but for each predicted link it decides whether it is a " topical” or a " social” link, and depending on this decision it produces a different type of explanation.
A topical link is recommended between a user interested in a topic and a user authoritative in that topic: the explanation in this case is a set of binary features describing the topic responsible of the link creation. A social link is recommended between users which share a large social neighborhood: in this case the explanation is the set of neighbors which are more likely to be responsible for the link creation.
Our experimental assessment on real-world data confirms the accuracy of WTFW in the link prediction and the quality of the associated explanations.
References
;
Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
---|---|---|---|---|---|---|---|---|---|---|
2014 WhotoFollowandWhyLinkPrediction | Francesco Bonchi Nicola Barbieri Giuseppe Manco | Who to Follow and Why: Link Prediction with Explanations | 10.1145/2623330.2623733 | 2014 |