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🎯 Rocchio Algorithm Demo: Relevance Feedback with Movie Plots

This is an interactive Python demo of the Rocchio Algorithm for relevance feedback in information retrieval. It uses a collection of movie plot summaries as the document corpus and shows how search results improve over multiple rounds of feedback.


πŸ” What is the Rocchio Algorithm?

The Rocchio Algorithm is a classic method used in information retrieval to refine a search query based on user feedback about which results are relevant or irrelevant.

[ \vec{q}{\text{new}} = \alpha \vec{q} + \frac{\beta}{|D_r|} \sum{\vec{d}r \in D_r} \vec{d}r - \frac{\gamma}{|D{nr}|} \sum{\vec{d}{nr} \in D{nr}} \vec{d}_{nr} ]

Where:

  • ( \vec{q} ): Original query vector
  • ( D_r ): Relevant documents
  • ( D_{nr} ): Irrelevant documents
  • ( \alpha, \beta, \gamma ): Weights (default: 1.0, 0.75, 0.25)

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