A new paper proposes framing Trajectory-Based Recommender Systems (TBRS) through the lens of control theory. The authors argue that TBRS, which focus on user trajectories and long-term goals, represent a distinct category of recommender systems that can be formalized and solved using control theory principles. The paper reviews existing work, highlights the unique characteristics of TBRS, and suggests that educational recommender systems, with their inherent long-term and goal-driven nature, can be effectively modeled within this proposed framework. AI
IMPACT Proposes a new theoretical framework for understanding and developing advanced recommender systems, potentially impacting personalized content delivery and educational tools.
RANK_REASON The item is a research paper submitted to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- arXiv
- CatalyzeX
- control theory
- CORE Recommender
- DagsHub
- Eriam Schaffter
- Gotit.pub
- Hugging Face
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- Long Term goal Recommender Systems
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- Trajectory-Based Recommender Systems
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