Social event planning using hybrid pairwise Markov random fields

Abstract

Event-based social networks (EBSNs) have become increasingly popular, which provide online social event management platforms for event organizers to publish and share social events (e.g., outdoor activities). In EBSNs, a major challenge for a social event organizer is how to plan a social event to attract the maximum number of attendance. To organize an event, three essential elements are required, namely, what (i.e., event content), where (i.e., event location), and when (i.e., event time). In this paper, we focus on the social event planning problem, which selects a location and time to hold a social event for the organizer with the given event content, to maximize the total number of participants. The solution of the social event planning problem could support decision-making for social event organizers. For simplicity, we denote a location and time pair as an item in this paper. To solve the social event planning problem, we present a hybrid pairwise Markov random field (H-PMRF) model which takes latent preferences of users, latent attributes of items, similarities between users and similarities between items into consideration. In particular, we construct an undirected graph where each node represents a user's decision on a specific item and each edge represents the relationship between the nodes, define the node potentials and edge potentials which model the dependency relationships between nodes, and give a joint probability distribution over the graph. Further, we adopt the Loopy Belief Propagation algorithm to compute the posterior probability distribution of each node in H-PMRF and select the location and time to hold the event which could attract the maximum number of participants. We collect real-world data set from DoubanEvent website and conduct extensive experiments on it. Experimental results show that the proposed model outperforms several baselines.