Name
Abstract | ||
|---|---|---|
Studies of online social behaviour indicate that users often fail to specify privacy settings that match their privacy behaviour. This issue has caused a dilemma whether to use publicly available data for targeted advertisement and personalization. As a possible approach to manage this dilemma, we propose a collaborative filtering method that exploits homophily to build a probabilistic model. Such a model can indicate the likelihood that a given public profile is meant to be private. Here, we provide the results of an analysis of a set of observable variables to be used in a neighbourhood-based manner. In addition, we establish a social graph augmented with privacy information. Users in the graph are then transformed into a set of latent features, uncovering informative factors to infer privacy preferences. |
Year | Venue | Field |
|---|---|---|
2016 | ICWSM | Data mining,Social graph,Computer science,Homophily,Artificial intelligence,Privacy software,Personalization,Collaborative filtering,Information retrieval,Inference,Neighbourhood (mathematics),Statistical model,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taraneh Khazaei | 1 | 16 | 4.42 |
| Lu Xiao | 2 | 38 | 9.44 |
| Robert E. Mercer | 3 | 254 | 46.93 |
| Atif Khan | 4 | 17 | 4.16 |