Evolving graph construction for successive recommendation in event-based social networks

Abstract

Personalized recommendation can help individual users to quickly reserve their interested events, which makes it indispensable in event-based social networks (EBSNs). However, as each EBSN is often with large amount of entities and each upcoming event is normally with non-repetitive uniqueness, how to deal with such challenges is crucial to the success of event recommendation. In this paper, we propose an evolving graph-based successive recommendation (EGSR) algorithm to address such challenges: The basic idea is to exploit the random walk with restart (RWR) on a recommendation graph for ranking the upcoming events. In EGSR, we employ a sliding window mechanism to construct evolving graphs for successively recommending new events for each user. We propose a graph entropy-based contribution measure for adjusting the window length and for weighting the history information. In EGSR, we also apply a topic analysis technique for analyzing event text description. We then propose to establish each user an interest model and to compute the similarities in between event content and user interest as edges’ weights for each recommendation graph. In successive recommendation, the number of upcoming events may experience great variations in different times. For a fair comparison, we also propose a set of cumulative evaluation metrics based on the traditional recommendation performance metrics. Experiments have been conducted based on the crawled one year data from a real EBSN for two cities. Results have validated the superiority of the proposed EGSR algorithm over the peer ones in terms of better recommendation performance and reduced computation complexity.