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