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STUDY: Socially Aware Temporally Causal Decoder Recommender Systems

arXiv 2306.07946 ↗First posted 2 Jun 2023cs.SI cs.AI cs.IRcited by 0

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Abstract (arXiv)

Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to use social network information effectively in recommender systems. This is especially true for demographic groups with interests that differ from the majority. This paper introduces STUDY, a Socially-aware Temporally caUsal Decoder recommender sYstem. STUDY introduces a new socially-aware recommender system architecture that is significantly more efficient to learn and train than existing methods. STUDY performs joint inference over socially connected groups in a single forward pass of a modified transformer decoder network. We demonstrate the benefits of STUDY in the recommendation of books for students who are dyslexic, or struggling readers. Dyslexic students often have difficulty engaging with reading material, making it critical to recommend books that are tailored to their interests. We worked with our non-profit partner Learning Ally to evaluate STUDY on a dataset of struggling readers. STUDY was able to generate recommendations that more accurately predicted student engagement, when compared with existing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 2 Jun 2023.

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References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Wide & Deep Learning for Recommender Systemscited by 747
  3. 2019Graph Neural Networks for Social Recommendationcited by 714
  4. 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 130
  5. 2019A Survey on Session-based Recommender Systemscited by 97
  6. 2019Embarrassingly Shallow Autoencoders for Sparse Datacited by 94
  7. 2019A Neural Influence Diffusion Model for Social Recommendationcited by 70
  8. 2018Sequence-Aware Recommender Systemscited by 58
  9. 2021Knowledge-aware Coupled Graph Neural Network for Social Recommendationcited by 49
  10. 2020Exploring Data Splitting Strategies for the Evaluation of Recommendation Modelscited by 48
  11. 2021Deep Learning for Click-Through Rate Estimationcited by 33
  12. 2021Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertisingcited by 31
2 more
  1. 2019FLEN: Leveraging Field for Scalable CTR Predictioncited by 3
  2. 2023Disentangled Graph Social Recommendationcited by 3

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