Current Focus

2025 Goals

Incorporate semantic IDs into a generalizable search and recommender system:

• Can function well with just a system prompt (guiding the overall goal of the system) and an item catalogue
• Handles a changing catalogue gracefully
• Good search and recommendation latency
• Cheap (runs on a T4 or equivalent)

Research Questions

• Optimizing LLM explanations based on implicit feedback

  • How to optimize an LLM to provide better recommendation explanations by fine-tuning on implicit feedback?

• Replacing BM25

  • How to design a search system that matches BM25 performance at cold start and gradually improves with more data, without dropping below BM25 performance?

• Precise Retrieval

  • The common two tower approach to embedding retrieval leaves much to be desired
    • There is no natural score threshold at which items are deemed irrelevant. Traditionally, classifiers have a 0.5 score cut-off.
    • Embedding retrieval tends to retrieve unrelated items. This is a well documented problem. For example, Nike shoes retrieves Adidas Shoes.
    • Can we have embedding models that approximate AND / OR conditions that more naturally fit into the retrieval paradigm?

• Efficient learning of semantic IDs

  • LLMs can learn Semantic IDs as part of their language and recommend and reason about items once they learn the "language" using Supervised Fine Tuning
  • But the danger is catastrophic forgetting and losing capabilities as they get fine tuned in this way
  • Is there a more "natural" way for LLMs to learn such semantic IDs?
  • How do we handle a changing catalogue in real-time gracefully?