Live streaming has become increasingly popular in recent years. Viewers of live streaming channels can interact with live streamers through various behaviors, such as sending virtual gifts and Danmaku. It is very critical to accurately model such viewers' behaviors, which reflect their interest, for recommending live streaming channels. However, existing studies on live streaming channel recommendation usually model viewers' interaction behaviors through traditional graphs where an edge only connects two nodes, which cannot capture interaction relationships between multi-viewers and multi-channels. In this study, we propose a novel approach to live streaming recommendation based on iewers' nteraction ehavior odeled by graphs (VIBM-Hyper). Specifically, VIBM-Hyper first constructs two hypergraphs to model viewers' interaction behaviors, including a channel-oriented behavior hypergraph and a viewer-oriented behavior hypergraph. Then, it employs a hypergraph convolution technique to learn the representations of viewers and live streaming channels, respectively, which are finally used to predict a viewer's preference for a certain live streaming channel. We analyzed viewers' multiple types of behaviors in live streaming channels and conducted empirical evaluation to investigate the effectiveness of VIBM-Hyper with two real-world datasets. The evaluation results demonstrate its superior performance in live streaming channel recommendation in comparison to the state-of-the-art methods.

中文翻译：

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基于观众互动行为的直播频道推荐：一种超图方法


近年来，直播越来越流行。直播频道的观众可以通过发送虚拟礼物、弹幕等多种行为与主播互动。准确地对这些观众的行为进行建模，反映他们的兴趣，对于推荐直播频道至关重要。然而，现有的直播频道推荐研究通常通过传统图来建模观众的交互行为，其中一条边仅连接两个节点，无法捕获多观众和多频道之间的交互关系。在这项研究中，我们提出了一种基于图解的用户交互行为的直播推荐新方法（VIBM-Hyper）。具体来说，VIBM-Hyper首先构建两个超图来对观众的交互行为进行建模，包括面向频道的行为超图和面向观众的行为超图。然后，它采用超图卷积技术分别学习观众和直播频道的表示，最终用于预测观众对某个直播频道的偏好。我们分析了直播频道中观看者的多种行为，并进行了实证评估，以利用两个真实数据集来研究 VIBM-Hyper 的有效性。评估结果表明，与最先进的方法相比，它在直播频道推荐方面具有优越的性能。