研究：具有社會意識的時間鬆散解碼推薦系統

摘要

隨著當今線上和線下數據量的劇增，推薦系統變得不可或缺，以幫助使用者找到符合其興趣的物品。當存在社交網絡信息時，有些方法利用這些信息來提供更好的推薦，然而這些方法通常具有複雜的架構和訓練程序。此外，許多現有方法使用圖神經網絡，而這些網絡訓練起來往往困難重重。為了應對這一問題，我們提出了具有社交感知和時間因果解碼器的推薦系統（STUDY）。STUDY通過修改後的Transformer解碼器網絡，在社交網絡圖中對相鄰用戶組進行聯合推論，僅需一次前向傳播。我們在基於學校教育內容的設定中測試我們的方法，利用課堂結構來定義社交網絡。我們的方法在保持單一同質網絡設計簡單性的同時，優於社交和順序方法，該網絡模擬了數據中的所有互動。我們還進行消融研究以了解我們性能提升的原因，發現我們的模型依賴於利用有效模擬用戶行為相似性的社交網絡結構。

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：具有社會意識的時間鬆散解碼推薦系統

STUDY: Socially Aware Temporally Casual Decoder Recommender Systems

摘要

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