Prompt Based Recommendation Systems: A New Era of Personalized Experiences
summary
LLM models like GPT-4 and seq2seq frameworks such as P5 treat recommendation as top-k prediction and explanation generation, producing next-item suggestions, rating estimates and conversational recommendations from user-item sequences. Experiments report listwise ranking failures (omitted items, repetition, unstable rankings) and persistent hallucination and bias risks. Proposed fixes include data de-biasing, fairness constraints via multi-objective optimization, and external audits plus real-time fairness monitoring using metrics like entropy, coverage and statistical parity.
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chapters (11)
- 0:00Introduction
- 1:11LLM based recommendation systems
- 3:16Prompt based recommendation systems
- 7:16P5 model framework
- 9:04Pointwise vs listwise prompting
- 12:18Fairness and bias challenges
- 15:13Hallucination in recommendations
- 17:47Solutions for fairness and bias
- 20:40Explainability and user control
- 22:03Ongoing efforts and future directions
- 24:09Conclusion and Q&A
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