Who to Follow and Why: Link Prediction with Explanations
| Citations: | 3 - 0 self |
BibTeX
@MISC{Barbieri_whoto,
author = {Nicola Barbieri and Francesco Bonchi},
title = {Who to Follow and Why: Link Prediction with Explanations},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
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 prob-lem 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 di↵erent type of explanation. A topical link is recommended between a user interested in a topic and a user authoritative in that topic: the expla-nation in this case is a set of binary features describing the topic responsible of the link creation. A social link is rec-ommended between users which share a large social neigh-borhood: 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.
Keyphrases
link prediction social link link creation large social neigh-borhood user recommendation topical link user authoritative experimental assessment social network nodes-attributed graph binary feature associated explanation stochastic topic model key component user recommender system real-world data on-line social networking platform di erent type
