Pyvorin for Recommendation Engines

Collaborative Filtering

User-user and item-item similarity scoring compiled for online serving.

def cosine_similarity(a, b):
    dot = 0.0
    norm_a = 0.0
    norm_b = 0.0
    for i in range(len(a)):
        dot += a[i] * b[i]
        norm_a += a[i] * a[i]
        norm_b += b[i] * b[i]
    return dot / ((norm_a * norm_b) ** 0.5)

Content-Based Scoring

TF-IDF and embedding-based similarity for item recommendations.

Two-Tower Models

Query and candidate tower scoring in retrieval pipelines.