Event-based online social platforms, such as Meetup and Plancast, have experienced increased popularity and rapid growth in recent years. In EBSN setup, selecting suitable venues for hosting events, which can attract a great turnout, is a key challenge. In this paper, we present a deep learning based venue recommendation system $DeepVenue$DeepVenue which provides context driven venue recommendations for the Meetup event-hosts to host their events. The crux of the proposed model relies on the notion of similarity between multiple Meetup entities such as events, venues, groups, etc. We develop deep learning techniques to compute a compact descriptor for each entity, such that two entities (say, venues) can be compared numerically. Notably, to mitigate the scarcity of venue related information in Meetup, we leverage on the cross domain knowledge transfer from popular LBSN service Yelp to extract rich venue related content. For hosting an event, the proposed $DeepVenue$DeepVenue model computes a success score for each candidate venue and ranks those venues according to the scores and finally recommend the top k venues. Our rigorous evaluation on the Meetup data collected for the city of Chicago shows that $DeepVenue$DeepVenue significantly outperforms the baselines algorithms. Precisely, for 84 percent of events, the correct hosting venue appears in the top 5 of the $DeepVenue$DeepVenue recommended list.

中文翻译：

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用于基于事件的社交网络的深度学习驱动的场所推荐器

近年来，Meetup 和 Plancast 等基于事件的在线社交平台越来越受欢迎且增长迅速。在 EBSN 设置中，选择合适的场地举办可以吸引大量观众的活动是一个关键挑战。在本文中，我们提出了一个基于深度学习的场地推荐系统$DeepVenue$D电子电子磷伏电子n你电子 为用户提供上下文驱动的场地建议 聚会活动主持人举办他们的活动。所提出模型的关键依赖于多个 Meetup 实体（例如事件、场所、团体等）之间的相似性概念。我们开发了深度学习技术来计算每个实体的紧凑描述符，以便两个实体（例如场所）可以进行数值比较。值得注意的是，为了缓解 Meetup 中场地相关信息的稀缺性，我们利用来自流行的 LBSN 服务 Yelp 的跨领域知识转移来提取丰富的场地相关内容。对于举办活动，建议$DeepVenue$D电子电子磷伏电子n你电子模型计算每个候选场地的成功分数，并根据分数对这些场地进行排名，最后推荐前 k 个场地。我们对为芝加哥市收集的 Meetup 数据进行的严格评估表明，$DeepVenue$D电子电子磷伏电子n你电子明显优于基线算法。准确地说，对于 84% 的活动，正确的举办地点出现在前 5 名$DeepVenue$D电子电子磷伏电子n你电子 推荐名单。