Build recommendation systems that understand user preferences deeply. Average what someone liked into a single query vector and let similarity do the ranking — no separate ML pipeline to stand up.
Average what someone liked, query with the result — runnable as-is on the bundled sample set
import numpy as np
from veep import Client, samples
df = samples.dataframe() # ~5,000 popular films with plot embeddings
vp = Client.login()
vp.collections.create("movie-recs", tier="hot")
vp.vectors.upsert("movie-recs", dataframe=df)
# Average a few liked films into a "taste centroid," then query with it
WATCH_LIST = ["film-00002", "film-00004", "film-00026"] # Lion King, Toy Story, Nemo
watched = np.stack([np.array(df[df.id == fid].iloc[0].vector) for fid in WATCH_LIST])
centroid = watched.mean(axis=0)
centroid = centroid / np.linalg.norm(centroid)
results = vp.vectors.query(
"movie-recs", centroid.tolist(), top_k=5,
filter={"id": {"$nin": WATCH_LIST}},
)
for r in results:
m = r.metadata
print(f"{r.score:.4f} {m.get('title','?')} ({m.get('year','?')})")The full walkthrough — every code block tested against the live service — is the Recommendations from a watch list tutorial.
Implement any algorithm with Vector Panda
Find patterns in user behavior. "Users who liked X also liked Y" recommendations powered by vector similarity.
Match item features to user preferences. Recommend similar items based on characteristics users have shown interest in.
Combine multiple signals for best results. Use collaborative, content, and contextual data together.
Real-time recommendations based on current session. Perfect for anonymous users or immediate context.
Use neural network embeddings for complex patterns. Integrate with transformers and other modern architectures.
Balance exploration and exploitation. Continuously optimize recommendations based on user feedback.
One collection on the standard Cohere 1M dataset, measured end to end through the public API with VectorDBBench. Real numbers, not projections — methodology and raw results here.
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