35 lines
1.5 KiB
Rust
35 lines
1.5 KiB
Rust
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use crate::affinity::TopicAffinities;
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/// Inputs to the final blended relevance score for one article.
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pub struct RelevanceInputs<'a> {
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/// Cosine similarity (0.0-1.0, already renormalized from [-1,1] if
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/// needed) between article and preference-profile embeddings.
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pub embedding_score: f32,
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/// LLM judgment (0.0-1.0), `None` if the article didn't clear the
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/// embedding shortlist threshold and so was never sent to the LLM.
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pub llm_score: Option<f32>,
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pub topics: &'a [String],
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pub affinities: &'a TopicAffinities,
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}
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/// Weights are deliberately conservative: the LLM judgment dominates when
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/// present (it has read the actual content), the embedding score is a
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/// fallback when the LLM stage was skipped, and topic affinity acts as a
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/// bounded nudge rather than a veto — a strong LLM match should still
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/// surface even for a topic the user historically skims.
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const W_LLM: f32 = 0.6;
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const W_EMBEDDING: f32 = 0.25;
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const W_AFFINITY: f32 = 0.15;
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pub fn score_article(inputs: RelevanceInputs) -> f32 {
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let affinity = inputs.affinities.score_topics(inputs.topics) as f32; // [-1, 1]
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let affinity_component = (affinity + 1.0) / 2.0; // renormalize to [0, 1]
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match inputs.llm_score {
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Some(llm) => W_LLM * llm + W_EMBEDDING * inputs.embedding_score + W_AFFINITY * affinity_component,
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// No LLM score yet: redistribute its weight onto the embedding score.
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None => (W_LLM + W_EMBEDDING) * inputs.embedding_score + W_AFFINITY * affinity_component,
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}
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.clamp(0.0, 1.0)
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}
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