Use Case Guide

Personalized Recommendations at Scale

Build recommendation systems that understand user preferences deeply. Vector Panda's distributed architecture delivers real-time personalization for millions of users and billions of items.

Live Recommendation Demo

See personalized recommendations in action

Recommended for You

98% match

Interstellar

A team of explorers travel through a wormhole in space in an attempt to ensure humanity's survival.

Because you watched Inception and love space exploration themes
94% match

Arrival

A linguist works with the military to communicate with alien lifeforms after twelve mysterious spacecraft appear.

Matches your interest in sci-fi with deep philosophical themes
91% match

Edge of Tomorrow

A soldier fighting aliens gets to relive the same day over and over again, getting better at combat each time.

Combines action with time-loop sci-fi elements you enjoy

Build Your Recommendation Engine

From user behavior to personalized suggestions

1. Create User and Item Embeddings

Generate embeddings that capture user preferences and item characteristics. Vector Panda handles the complexity of storing and querying at scale.

Python
import veep
import numpy as np
from sklearn.decomposition import TruncatedSVD
panda = veep.Client("your-api-key")
# Create collections for users and items
users = panda.collection("users")
items = panda.collection("items")
# Generate embeddings from interaction matrix
def create_embeddings(interaction_matrix):
svd = TruncatedSVD(n_components=128)
user_embeddings = svd.fit_transform(interaction_matrix)
item_embeddings = svd.components_.T
return user_embeddings, item_embeddings
# Store user embeddings with metadata
for user_id, embedding in enumerate(user_embeddings):
users.upsert(
id=f"user_{user_id}",
vector=embedding,
metadata={
"user_id": user_id,
"preferences": user_preferences[user_id],
"segment": user_segments[user_id]
}
)

2. Real-time Collaborative Filtering

Find similar users and items in real-time. Vector Panda's 100% recall ensures you never miss relevant recommendations.

Python
def get_user_recommendations(user_id, k=10):
# Get user's current embedding
user_vector = users.get(user_id).vector
# Find similar users
similar_users = users.search(
vector=user_vector,
k=50,
exclude_ids=[user_id]
)
# Get items liked by similar users
candidate_items = []
for similar_user in similar_users:
liked_items = get_user_liked_items(similar_user.id)
candidate_items.extend(liked_items)
# Score and rank recommendations
item_scores = {}
for item_id in set(candidate_items):
item_vector = items.get(item_id).vector
score = np.dot(user_vector, item_vector)
item_scores[item_id] = score
# Return top recommendations
recommendations = sorted(
item_scores.items(),
key=lambda x: x[1],
reverse=True
)[:k]
return recommendations

3. Content-Based Filtering

Combine user preferences with item features for hybrid recommendations. Use metadata filters for business rules and constraints.

Python
# Content-based recommendations with constraints
def content_based_recommendations(
user_id,
categories=None,
exclude_watched=True,
min_rating=4.0
):
# Build user preference vector
user_history = get_user_history(user_id)
preference_vector = build_preference_vector(user_history)
# Set up filters
filters = {"rating": {"$gte": min_rating}}
if categories:
filters["category"] = {"$in": categories}
exclude_ids = []
if exclude_watched:
exclude_ids = get_user_watched_items(user_id)
# Search with filters
recommendations = items.search(
vector=preference_vector,
k=20,
filters=filters,
exclude_ids=exclude_ids,
include_metadata=True
)
# Add explanation for each recommendation
for rec in recommendations:
rec.explanation = generate_explanation(
user_history,
rec.metadata
)
return recommendations

4. Real-time Updates and Learning

Update user preferences and item popularity in real-time. Vector Panda's instant updates mean recommendations improve with every interaction.

Python
# Update user embedding after interaction
def update_user_preferences(user_id, item_id, action):
# Get current embeddings
user_vec = users.get(user_id).vector
item_vec = items.get(item_id).vector
# Update based on action
if action == "like":
learning_rate = 0.1
elif action == "view":
learning_rate = 0.05
else: # dislike
learning_rate = -0.1
# Update user embedding
new_user_vec = user_vec + learning_rate * item_vec
new_user_vec = new_user_vec / np.linalg.norm(new_user_vec)
# Store updated embedding immediately
users.upsert(
id=user_id,
vector=new_user_vec,
metadata={
"last_action": action,
"last_item": item_id,
"updated_at": datetime.now()
}
)
# Update item popularity
update_item_stats(item_id, action)

Recommendation Algorithms

Implement any algorithm with Vector Panda

🤝

Collaborative Filtering

Find patterns in user behavior. "Users who liked X also liked Y" recommendations powered by vector similarity.

  • E-commerce product recommendations
  • Movie and music suggestions
  • Social media content feeds
📊

Content-Based

Match item features to user preferences. Recommend similar items based on characteristics users have shown interest in.

  • News article recommendations
  • Job matching platforms
  • Educational content
🔄

Hybrid Systems

Combine multiple signals for best results. Use collaborative, content, and contextual data together.

  • Netflix-style recommendations
  • Spotify Discover Weekly
  • Amazon product suggestions
📈

Session-Based

Real-time recommendations based on current session. Perfect for anonymous users or immediate context.

  • E-commerce browsing
  • Content discovery
  • Travel planning
🧠

Deep Learning

Use neural network embeddings for complex patterns. Integrate with transformers and other modern architectures.

  • Video recommendations
  • Fashion and style
  • Gaming matchmaking
🎯

Multi-Armed Bandit

Balance exploration and exploitation. Continuously optimize recommendations based on user feedback.

  • Homepage personalization
  • Email campaigns
  • Ad targeting

Performance at Scale

Real metrics from production deployments

8ms
P99 Latency
Real-time recommendations even at peak load
35%
CTR Improvement
Average increase in click-through rates
100M
Daily Recommendations
Served across all customer deployments
10B+
Item Catalog Size
Largest deployment without performance loss

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