Exploiting multiple influence pattern of event organizer for event recommendation

Existing event recommendation methods pay attention to contextual factors to approach sparse and cold-start problem, in which organizer influence is a vital factor in Event-based Social Networks (EBSNs). However, existing studies ignore multiple influence pattern of organizer at event-level. In this paper, we distinguish organizer role and user (participant) role, exploring the organizer multiple influence pattern at event-level based on two scores: organizer behavior score and organizer popularity score. Besides, the organizer influence at event-level is dynamic, the step length is the time difference between two adjacent events from same organizer. Based on this discovery, we first calculate the organizer behavior score and organizer popularity score, then we propose an Organizer Multiple Influence Pattern-aware model (OMIP) based on topic model to capture user event topic preferences under the multiple influence pattern, which models the correlation and alternative-relation between user behavior topic and influence pattern. OMIP depends on the user’s participation records, user’s profiles and organizer’s profiles. OMIP outperforms state-of-the-art baselines with remarkable improvements in terms of Recall@k, NDCG@k, F1@k, and AUC. Specifically, Recall@5 improvement of 0.22%–16.41%; NDCG@5 improvement of 1.25%–10.81%; F1@5 improvement of 3.49%–16.43%; AUC improvement of 0.70%–1.62% on two real-world EBSNs datasets. Besides, OMIP can learn semantically topics and patterns which are useful to explain recommendations.