Two-Tower and FAISS: AI Recommendation System for Personalization

A new user enters a marketplace — the system doesn't know their tastes. They see popular products, but conversion drops. This is a typical cold-start. The Two-Tower + FAISS architecture is a proven production-ready approach that delivers CTR of 8-15% and additional revenue of 12-25%. We have implemented dozens of such projects, from startups to enterprise.

Let's consider the key problems: cold-start, scaling to millions of transactions, balancing accuracy and latency. Each requires a separate solution. Production-ready development of an AI recommendation system is built on a Two-Tower neural network and a FAISS index.

How Two-Tower solves the cold start problem?

Cold start for new users is a typical pain point. Without purchase history, the system displays random items, conversion drops. Our approach uses semantic embeddings of items and demographic features to provide relevant recommendations from the first visit. Matrix Factorization works, but only for users with history — for cold start, content features are needed.

We use a hybrid approach: content-based filtering based on text embeddings (BERT, 768-dim) for new users, and after accumulating 3-5 interactions, we switch to collaborative filtering. Recall@10 for cold users reaches 20% vs 5% for pure MF.

Why is Two-Tower architecture more effective than content-based?

Two-tower allows learning user and item embeddings in a single space, training on implicit feedback (clicks, views). This yields CTR of 8-15% vs 2-4% for rule-based systems. On large volumes (>5M transactions), early fusion does not scale — two-tower with in-batch negatives trains in 2-3 hours on 8 V100.

Comparison of metrics before and after implementation

As a result of implementation, additional revenue increases by 12-25%, which for a large e-commerce platform can amount to millions of rubles per month.

Technical implementation: Two-Tower, training and inference

Two-Tower Architecture

import torch import torch.nn as nn import numpy as np from torch.utils.data import DataLoader, Dataset class UserTower(nn.Module): """User encoder""" def __init__(self, n_users: int, n_categories: int, embedding_dim: int = 64, hidden_dim: int = 128): super().__init__() self.user_emb = nn.Embedding(n_users + 1, embedding_dim, padding_idx=0) self.category_emb = nn.Embedding(n_categories + 1, 16, padding_idx=0) self.mlp = nn.Sequential( nn.Linear(embedding_dim + 16 * 5 + 10, hidden_dim), nn.ReLU(), nn.Dropout(0.3), nn.Linear(hidden_dim, 64), nn.LayerNorm(64) ) def forward(self, user_id, top_categories, behavior_features): user_vec = self.user_emb(user_id) cat_vecs = self.category_emb(top_categories).view(top_categories.shape[0], -1) combined = torch.cat([user_vec, cat_vecs, behavior_features], dim=1) return self.mlp(combined) class ItemTower(nn.Module): """Item encoder""" def __init__(self, n_items: int, n_categories: int, embedding_dim: int = 64, text_dim: int = 128): super().__init__() self.item_emb = nn.Embedding(n_items + 1, embedding_dim, padding_idx=0) self.category_emb = nn.Embedding(n_categories + 1, 16, padding_idx=0) self.mlp = nn.Sequential( nn.Linear(embedding_dim + 16 + text_dim + 5, 128), nn.ReLU(), nn.Dropout(0.3), nn.Linear(128, 64), nn.LayerNorm(64) ) def forward(self, item_id, category_id, text_features, item_features): item_vec = self.item_emb(item_id) cat_vec = self.category_emb(category_id) combined = torch.cat([item_vec, cat_vec, text_features, item_features], dim=1) return self.mlp(combined) class TwoTowerModel(nn.Module): def __init__(self, n_users, n_items, n_categories, text_dim=128): super().__init__() self.user_tower = UserTower(n_users, n_categories) self.item_tower = ItemTower(n_items, n_categories, text_dim=text_dim) self.temperature = nn.Parameter(torch.ones(1) * 0.05) def forward(self, user_inputs, item_inputs): user_emb = self.user_tower(**user_inputs) item_emb = self.item_tower(**item_inputs) # Cosine similarity user_norm = nn.functional.normalize(user_emb, dim=1) item_norm = nn.functional.normalize(item_emb, dim=1) scores = torch.sum(user_norm * item_norm, dim=1) / self.temperature return scores def get_user_embedding(self, user_inputs) -> torch.Tensor: with torch.no_grad(): return nn.functional.normalize(self.user_tower(**user_inputs), dim=1) def get_item_embedding(self, item_inputs) -> torch.Tensor: with torch.no_grad(): return nn.functional.normalize(self.item_tower(**item_inputs), dim=1) 

In-Batch Negative Sampling Training

class RecommendationTrainer: def __init__(self, model: TwoTowerModel, lr: float = 1e-3): self.model = model self.optimizer = torch.optim.Adam(model.parameters(), lr=lr) self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( self.optimizer, T_max=10 ) def train_epoch(self, dataloader: DataLoader) -> float: self.model.train() total_loss = 0 for batch in dataloader: user_inputs, pos_item_inputs = batch['user'], batch['positive_item'] # User embeddings: [batch_size, dim] user_embs = nn.functional.normalize( self.model.user_tower(**user_inputs), dim=1 ) # Item embeddings: [batch_size, dim] item_embs = nn.functional.normalize( self.model.item_tower(**pos_item_inputs), dim=1 ) # In-batch negatives: similarity matrix [batch_size x batch_size] scores = torch.matmul(user_embs, item_embs.T) / self.model.temperature # Diagonal = positive examples labels = torch.arange(len(user_embs)).to(scores.device) loss = nn.functional.cross_entropy(scores, labels) self.optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) self.optimizer.step() total_loss += loss.item() self.scheduler.step() return total_loss / len(dataloader) 

ANN Index for Real-Time

import faiss class RecommendationIndex: """Index for fast top-K recommendation search""" def __init__(self, dim: int = 64, n_items: int = 100000): # IVF-PQ index: speed/accuracy tradeoff quantizer = faiss.IndexFlatIP(dim) n_lists = min(int(np.sqrt(n_items)), 4096) self.index = faiss.IndexIVFPQ(quantizer, dim, n_lists, 8, 8) self.index.nprobe = 32 # Number of clusters to search self.item_ids = [] def build(self, item_embeddings: np.ndarray, item_ids: list): """Build index from embeddings""" embeddings_norm = item_embeddings / np.linalg.norm( item_embeddings, axis=1, keepdims=True ) self.index.train(embeddings_norm.astype(np.float32)) self.index.add(embeddings_norm.astype(np.float32)) self.item_ids = item_ids def recommend(self, user_embedding: np.ndarray, k: int = 50, exclude_ids: list = None) -> list[tuple]: """Top-K recommendations for a user""" user_norm = user_embedding / np.linalg.norm(user_embedding) scores, indices = self.index.search( user_norm.reshape(1, -1).astype(np.float32), k + (len(exclude_ids) if exclude_ids else 0) + 10 ) results = [] for score, idx in zip(scores[0], indices[0]): if idx < 0: continue item_id = self.item_ids[idx] if exclude_ids and item_id in exclude_ids: continue results.append((item_id, float(score))) if len(results) >= k: break return results 

Re-ranking and Business Rules

class RecommendationReranker: """Apply business logic to candidates from retrieval model""" def rerank(self, candidates: list[tuple], user_context: dict, business_rules: dict) -> list[tuple]: """ candidates: [(item_id, base_score)] business_rules: {'boost_new_items': 1.2, 'boost_on_sale': 1.15, 'max_same_category': 3} """ scored_candidates = [] category_count = {} for item_id, base_score in candidates: item = self._get_item_metadata(item_id) if item is None: continue # Apply boosts score = base_score if business_rules.get('boost_new_items') and item.get('is_new'): score *= business_rules['boost_new_items'] if business_rules.get('boost_on_sale') and item.get('on_sale'): score *= business_rules['boost_on_sale'] # Category limit cat = item.get('category') max_per_cat = business_rules.get('max_same_category', 5) if category_count.get(cat, 0) >= max_per_cat: continue category_count[cat] = category_count.get(cat, 0) + 1 scored_candidates.append((item_id, score)) return sorted(scored_candidates, key=lambda x: x[1], reverse=True) 

Typical metrics after implementing a recommendation system: CTR of recommendations 8-15% (vs 2-4% for popular items), conversion from recommendations 3-7%, additional revenue 12-25% of total turnover. Cold start time for new users without history: 3-5 interactions to receive personalized recommendations.

Process, scope, and cost

1. Analytics: data audit, user profiling, defining business goals.
2. Design: choosing architecture (content-based, collaborative, two-tower), training pipelines.
3. Development: implementing models (PyTorch), training on historical data, setting up ANN index (FAISS).
4. Integration: REST API, gRPC, containerization (Docker), deployment in Kubernetes.
5. A/B testing: metrics (Recall, NDCG, revenue share), monitoring data drift.
6. Support: 12 months warranty, model updates, business rules refinement.

What is included in the work

• Architectural document (approach selection, justification)
• Source code for models and pipelines (PyTorch, FAISS)
• Docker images and Kubernetes manifests
• REST API / gRPC for real-time inference
• Integration with your CRM/website
• Documentation and team training
• A/B testing and monitoring setup (Prometheus + Grafana)
• 12 months of support and updates

Timeline: from 30 to 60 working days depending on data volume and integration complexity. Cost is calculated individually — we will evaluate your project in 1 day after reviewing the task.

We have dozens of implemented projects — from startups to enterprise. Additional revenue after implementation amounts to millions of rubles per month. Order the development of an AI recommendation system and get a consultation on your project architecture.