Personalized Recommendation Algorithm

A Personalized Recommendation Algorithm is a recommendations algorithm that is a personalized ranking algorithm.

• Context:
  • It can be implemented by a Personalized Recommendation System (that solves a personalized recommendation task).
  • It can (typically) be a Data-Driven Personalized Item Recommendations Algorithm.
  • It can range from being a Micro-Personalized Item Recommendation Algorithm to being a Macro-Personalized Item Recommendation Algorithm.
  • It can range from being a Contextual Personalized Item Recommendation Algorithm to being a Non-Contextual Personalized Item Recommendation Algorithm.
  • It can range from being a Realtime Personalized Item Recommendation Algorithm to being a Lag-Timed Personalized Item Recommendation Algorithm.
  • It can range from being a Session-Aware Personalized Item Recommendation Algorithm to being a Non-Session-Aware Personalized Item Recommendation Algorithm.
  • …
• Example(s):
  • Collaborative Filtering Algorithm, implemented in a data-driven item recommendations system.
  • …
• Counter-Example(s):
  • a Non-Personalized Recommender Algorithm.
• See: Personalized Web Search Algorithm.

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References

2018

• (Wang et al., 2018) ⇒ Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, and Tat-Seng Chua. (2018). “TEM: Tree-enhanced Embedding Model for Explainable Recommendation.” In: Proceedings of the 2018 World Wide Web Conference.
  • QUOTE: ... While collaborative filtering is the dominant technique in personalized recommendation, it models user-item interactions only and cannot provide concrete reasons for a recommendation. ...

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