EBSN中基于潜在好友关系的活动推荐算法

Activity Recommendation Algorithm Based on Latent Friendships in EBSN

EBSN(Event-based Social Networks)与传统社交网络有所不同,它不仅包含传统社交网中的线上交互(Online Interactions),还包含颇具价值的线下交互(Offline Interactions),是一种异构型复杂社交网络。如何有效利用这种虚拟与物理相融合的交互关系来提高活动推荐服务的质量,是目前学术界和工业界共同关注的热点研究问题之一。传统社交活动推荐算法,如基于用户偏好或线上好友关系的活动推荐算法,除了考虑活动和用户的基本属性外,大多基于显式好友关系EF(Explicit Friendship)进行活动推荐,但EBSN不具备显式好友关系,因此上述算法均不能直接用于EBSN活动推荐。为此,定义了一种新的潜在好友关系LF(Latent Friendship),LF关系将线上同组、线下同活动综合纳入活动评分计算中,以体现LF对EBSN活动推荐的影响;同时,基于此提出了一种基于潜在好友关系的EBSN活动推荐算法(Activity Recommendation Algorithm based on Latent Friendships,ARLF),该算法在寻找潜在好友关系时,创新性地运用元路径思想,使得EBSN中的异构信息得到了充分利用。最后,利用Meetup事件社交网中的真实数据对ARLF算法进行了性能测试,可扩展性实验证明了该算法是可行且有效的。

EBSN(Event-based Social Networks)not only contain online social interactions in the conventional online social networks,but also include valuable offline social interactions captured in offline activities,possessing a complicated heterogeneous nature.How to use the coexistence of online and offline social interactions to improve the service quality has become a hot issue in both academic and industry domains.Besides considering basic attributes from events and users,most traditional social activity recommendation approaches recommend interesting activities to users based on explicit friendships.However,there is no explicit friendships in EBSN,which makes these traditional algorithms can't be applied to EBSN's event recommendation directly.In this light,a novel concept LF(Latent Friendship)was defined in this paper.LF not only takes into account the online same group relationships,but also considers the offline same activity relationships.Further,ARLF(Activity Recommendation Algorithm based on Latent Friendship)was proposed by synthesizing the influence of group and activity for activity recommendation.Meanwhile,this paper creatively applied the idea of Meta-Path to capture the latent friends,which exploits the heterogeneous information fully in EBSN.Finally,extensive experiments based on real data of Meetup show that ARLF is feasible and effective on recommending desirable and interesting activities for EBSN users.