Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user’s preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user’s demand on the news diversity. We have three mainsteps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user’s interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

互动新闻推荐近期上线，备受关注。在这种场景下，用户的行为从单击行为演变为包括点赞、评论、分享等多种行为。然而，现有的方法大部分仍然使用单击行为作为判断用户偏好的唯一标准。此外，虽然异构图已经应用于不同的领域，但仍然需要一种适当的方法来构建具有适当学习机制的交互式新闻数据的异构图。为了解决上述问题，我们提出了一个基于图的行为感知网络，它同时考虑了六种不同类型的行为以及用户对新闻多样性的需求。我们有三个主要步骤。首先，我们为多级和多类别数据构建一个交互行为图。其次，我们在行为图上应用 DeepWalk 以获得实体语义，然后构建一个称为 G-CNN 的基于图的卷积神经网络来学习新闻表示，以及一个基于注意力的 LSTM 来学习行为序列表示。第三，我们为行为图引入了核心和相关性特征，用于衡量用户兴趣的集中程度。这些特征会影响我们个性化推荐系统的准确性和多样性之间的权衡。考虑到这些特点，我们的系统最终实现了向不同用户的不同专注度推荐新闻。和基于注意力的 LSTM 来学习行为序列表示。第三，我们为行为图引入了核心和相关性特征，用于衡量用户兴趣的集中程度。这些特征会影响我们个性化推荐系统的准确性和多样性之间的权衡。考虑到这些特点，我们的系统最终实现了向不同用户的不同专注度推荐新闻。和基于注意力的 LSTM 来学习行为序列表示。第三，我们为行为图引入了核心和相关性特征，用于衡量用户兴趣的集中程度。这些特征会影响我们个性化推荐系统的准确性和多样性之间的权衡。考虑到这些特点，我们的系统最终实现了向不同用户的不同专注度推荐新闻。