通过社交感知推荐提高智能会议参与度
Improving Smart Conference Participation through Socially-Aware Recommendation
摘要：
这项研究致力于向参与者推荐智能会议上的演示文稿。我们提出了一个场地推荐算法，社会意识的场地和环境推荐（SARVE）。SARVE计算会议参与者的相关性和社会特征信息。为了使用分布式社区发现对推荐过程进行建模，SARVE进一步集成了智能会议社区和参与者的当前上下文。SARVE推荐每个参与者可能高度感兴趣的演示文稿。我们使用真实世界的数据集来评估SARVE。在我们的实验中，我们比较SARVE两个相关的国家的最先进的方法，即：上下文感知移动推荐服务（CAMRS）和会议导航（推荐）模型。我们的实验结果表明，在所使用的评价指标：精度，召回率和f-测量，SARVE实现更可靠和有利的社会（关系和上下文）推荐结果。
英文摘要：
This research addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm, Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real world dataset. In our experiments, we compare SARVE to two related state-of-the-art methods, namely: Context-Aware Mobile Recommendation Services (CAMRS) and Conference Navigator (Recommender) Model. Our experimental results show that in terms of the utilized evaluation metrics: precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
