title: STUDY: Socially Aware Temporally Casual Decoder Recommender Systems
summary: 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.
paper_authors._id: 64893195b168777dcf752428
paper_authors.name: Eltayeb Ahmed
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T12:10:36.771Z
paper_authors._id: 637f4f35b3cb8158e17320ac
paper_authors.avatarUrl: /avatars/dc50dc74ec345a86b8f9a1352bc3f16c.svg
paper_authors.fullname: Eltayeb Ahmed
paper_authors.type: user
paper_authors.user: clockwork7
paper_authors._id: 64893195b168777dcf752429
paper_authors.name: Diana Mincu
paper_authors._id: 64893195b168777dcf75242a
paper_authors.name: Lauren Harrell
paper_authors._id: 64893195b168777dcf75242b
paper_authors.name: Katherine Heller
paper_authors._id: 64893195b168777dcf75242c
paper_authors.name: Subhrajit Roy
paper_title: STUDY: Socially Aware Temporally Casual Decoder Recommender Systems
paper_summary: 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.
paper_ai_summary: Socially-aware Temporally caUsal Decoder recommender system (STUDY) achieves high performance using a single transformer decoder network by leveraging social network structure.
paper_ai_keywords: graph neural networks transformer decoder network social network graph joint inference ablation studies user behavior