标题： 基于潜在网络融合的离线瞬时社交网络事件推荐模型 显示英文标题

标题： Latent Networks Fusion based Model for Event Recommendation in Offline Ephemeral Social Networks

摘要： 随着移动社交媒体数量的增加，离线短暂社交网络（OffESNs）正受到越来越多的关注。 离线短暂社交网络（OffESNs）是在特定地点为特定目的临时创建的网络，持续时间较短，依赖于如射频识别（RFID）和蓝牙设备等移动社交媒体。 人们在OffESNs中的主要目的是通过参加预定活动来获取和分享信息。 在这种网络上进行活动推荐可以协助参与者选择预定活动，以及帮助组织者进行资源规划。 然而，由于缺乏用户偏好和评分信息，以及明确的社会关系，基于评分的传统推荐方法和社会信任基于的推荐方法都无法很好地在OffESNs中进行活动推荐。 为了解决诸如如何推导用户的潜在偏好和社会关系，以及如何在统一模型中融合潜在信息等挑战，我们首先构建了两个异构交互社交网络，一个是活动参与网络，另一个是物理接近网络。 然后，我们利用它们来推导用户的潜在偏好和潜在的社会关系网络，包括志同道合的同伴、共同参加者和朋友。 最后，我们在成对因子图下提出了一种LNF（潜在网络融合）模型，以推断活动参与概率用于推荐。 在基于RFID的真实会议数据集上的实验表明，所提出的模型相比典型解决方案更为有效。 显示英文摘要

摘要： With the growing amount of mobile social media, offline ephemeral social networks (OffESNs) are receiving more and more attentions. Offline ephemeral social networks (OffESNs) are the networks created ad-hoc at a specific location for a specific purpose and lasting for short period of time, relying on mobile social media such as Radio Frequency Identification (RFID) and Bluetooth devices. The primary purpose of people in the OffESNs is to acquire and share information via attending prescheduled events. Event Recommendation over this kind of networks can facilitate attendees on selecting the prescheduled events and organizers on making resource planning. However, because of lack of users preference and rating information, as well as explicit social relations, both rating based traditional recommendation methods and social-trust based recommendation methods can no longer work well to recommend events in the OffESNs. To address the challenges such as how to derive users latent preferences and social relations and how to fuse the latent information in a unified model, we first construct two heterogeneous interaction social networks, an event participation network and a physical proximity network. Then, we use them to derive users latent preferences and latent networks on social relations, including like-minded peers, co-attendees and friends. Finally, we propose an LNF (Latent Networks Fusion) model under a pairwise factor graph to infer event attendance probabilities for recommendation. Experiments on an RFID-based real conference dataset have demonstrated the effectiveness of the proposed model compared with typical solutions.