研究：具有社会意识的时间松散解码推荐系统

摘要

随着当今在线和离线数据数量的剧增，推荐系统变得尤为重要，帮助用户找到符合其兴趣的物品。当存在社交网络信息时，有一些方法利用这些信息来提供更好的推荐，然而这些方法通常具有复杂的架构和训练程序，显得笨重。此外，许多现有方法使用图神经网络，这些网络训练起来往往很困难。为了解决这个问题，我们提出了基于社交感知的时间因果解码推荐系统（STUDY）。STUDY通过修改后的Transformer解码器网络，在社交网络图中对相邻用户组进行联合推断，只需进行一次前向传播。我们在基于学校教育内容的场景中测试了我们的方法，利用课堂结构定义社交网络。我们的方法在保持单一同质网络设计简单性的同时，胜过了社交和顺序方法，模拟了数据中的所有交互。我们还进行了消融研究，以了解我们性能提升的驱动因素，并发现我们的模型依赖于利用有效模拟用户行为相似性的社交网络结构。

English

With the overwhelming amount of data available both on and offline today, recommender systems have become much needed to help users find items tailored to their interests. When social network information exists there are methods that utilize this information to make better recommendations, however the methods are often clunky with complex architectures and training procedures. Furthermore many of the existing methods utilize graph neural networks which are notoriously difficult to train. To address this, we propose Socially-aware Temporally caUsal Decoder recommender sYstems (STUDY). STUDY does joint inference over groups of users who are adjacent in the social network graph using a single forward pass of a modified transformer decoder network. We test our method in a school-based educational content setting, using classroom structure to define social networks. Our method outperforms both social and sequential methods while maintaining the design simplicity of a single homogeneous network that models all interactions in the data. We also carry out ablation studies to understand the drivers of our performance gains and find that our model depends on leveraging a social network structure that effectively models the similarities in user behavior.

研究：具有社会意识的时间松散解码推荐系统

STUDY: Socially Aware Temporally Casual Decoder Recommender Systems

摘要

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