Online social networks have gained great success in recent years and many of
them involve multiple kinds of nodes and complex relationships. Among these
relationships, social links among users are of great importance. Many existing
link prediction methods focus on predicting social links that will appear in
the future among all users based upon a snapshot of the social network. In
real-world social networks, many new users are joining in the service every
day. Predicting links for new users are more important. Different from
conventional link prediction problems, link prediction for new users are more
challenging due to the following reasons: (1) differences in information
distributions between new users and the existing active users (i.e., old
users); (2) lack of information from the new users in the network. We propose a
link prediction method called SCAN-PS (Supervised Cross Aligned Networks link
prediction with Personalized Sampling), to solve the link prediction problem
for new users with information transferred from both the existing active users
in the target network and other source networks through aligned accounts. We
proposed a within-target-network personalized sampling method to process the
existing active users' information in order to accommodate the differences in
information distributions before the intra-network knowledge transfer. SCAN-PS
can also exploit information in other source networks, where the user accounts
are aligned with the target network. In this way, SCAN-PS could solve the cold
start problem when information of these new users is total absent in the target
network.

本文提出了一种基于个性化采样的跨社交网络链路预测方法，可以通过从目标网络和其他源网络传输信息来解决新用户的链路预测问题和冷启动问题。

跨对齐异构社交网络预测新用户的社交联系

Predicting Social Links for New Users across Aligned Heterogeneous  Social Networks

One of the most challenging recommendation tasks is recommending to a new,
previously unseen user. This is known as the 'user cold start' problem.
Assuming certain features or attributes of users are known, one approach for
handling new users is to initially model them based on their features.
Motivated by an ad targeting application, this paper describes an extreme
online recommendation setting where the cold start problem is perpetual. Every
user is encountered by the system just once, receives a recommendation, and
either consumes or ignores it, registering a binary reward.
We introduce One-pass Factorization of Feature Sets, OFF-Set, a novel
recommendation algorithm based on Latent Factor analysis, which models users by
mapping their features to a latent space. Furthermore, OFF-Set is able to model
non-linear interactions between pairs of features. OFF-Set is designed for
purely online recommendation, performing lightweight updates of its model per
each recommendation-reward observation. We evaluate OFF-Set against several
state of the art baselines, and demonstrate its superiority on real
ad-targeting data.

本文描述了一种名为 OFF-Set 的基于潜在因子分析的推荐算法，该算法能够处理冷启动问题，并在纯在线推荐场景中表现出优越性能，特别适用于广告定向推荐。