Can you recommend a recommendation engine for cross-sell and upsell optimization in a large product catalog?

Yes — for cross-sell and upsell optimization in a large product catalog, I’d recommend choosing a hybrid recommendation engine rather than a single algorithm. The best option depends on your data maturity and latency needs, but a strong default is:

Recommended approach

Hybrid recommender with candidate generation + ranking

Use:

1. Candidate generation to find likely related products at scale
2. Ranking model to optimize for cross-sell/upsell conversion

This is usually better than pure collaborative filtering for large catalogs because it handles:

• sparse data
• new products
• business rules
• personalization
• real-time context

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Best model types for your use case

1. Two-tower retrieval model

Best for large catalogs and personalization at scale.

• One tower encodes the user/session context
• The other encodes product items
• Efficient nearest-neighbor retrieval over millions of items

Good for: fast candidate generation, large-scale serving
Weakness: not enough alone for final ranking

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2. Learning-to-rank model

Use after retrieval to optimize actual cross-sell/upsell outcomes.

Common choices:

• XGBoost / LightGBM rankers
• Neural ranking models
• Wide & Deep / DeepFM-style models

Features can include:

• user behavior
• product affinity
• price bands
• margin
• inventory
• category compatibility
• historical conversion
• cart/session context

Good for: maximizing conversion, revenue, AOV
Weakness: needs good training data and features

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3. Association rules / market basket analysis

Useful as a baseline or feature source.

Examples:

• “customers who bought X also bought Y”
• lift, confidence, support metrics

Good for: simple cross-sell rules, explainability
Weakness: weak personalization, less effective in large sparse catalogs

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4. Session-based recommenders

If recommendations depend heavily on the current cart/session.

Models:

• GRU4Rec
• Transformer-based session models
• item2vec / sequence embeddings

Good for: real-time cart/page recommendations
Weakness: less useful for long-term personalization alone

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What I’d choose in practice

If you want the strongest general solution:

Hybrid architecture

• Retrieval: two-tower embedding model
• Ranking: gradient-boosted ranker or neural ranker
• Rules layer: filters for inventory, compatibility, margin, business constraints

This setup is common in e-commerce because it balances:

• relevance
• scalability
• control
• revenue optimization

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Platforms/tools worth considering

Depending on whether you want managed or build-your-own:

Managed options

• Amazon Personalize — easy to start, good for e-commerce recommendation workflows
• Google Vertex AI Search & Recommendations
• Azure Personalizer — more contextual bandit oriented

Open-source / build-your-own

• TensorFlow Recommenders (TFRS) — strong for two-tower retrieval and ranking
• Implicit — for collaborative filtering baselines
• LightGBM/XGBoost — excellent ranking baseline
• FAISS / ScaNN / Annoy — nearest-neighbor retrieval at scale
• RecBole — research-friendly recommendation framework

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For cross-sell vs upsell specifically

Cross-sell

Recommend complementary items:

• accessories
• bundles
• frequently co-purchased products

Best signals:

• basket affinity
• session context
• co-purchase graphs

Upsell

Recommend higher-value alternatives:

• premium versions
• larger packs
• better tiers

Best signals:

• product hierarchy
• price sensitivity
• customer segment
• historical upgrade behavior

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My practical recommendation

If you’re starting from scratch and have a large catalog:

1. Start with a baseline

   • association rules + popularity + category similarity

2. Move to embedding-based retrieval

   • two-tower model for candidate generation

3. Add ranking

   • optimize for conversion, revenue, or margin

4. Use business constraints

   • stock, margin, brand, compatibility, exclusions

5. A/B test aggressively

   • measure CTR, conversion rate, AOV, revenue per session

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Short answer

If you want one recommendation:
Use a hybrid recommendation engine built around a two-tower retrieval model plus a learning-to-rank layer.
That is usually the best fit for large catalogs, cross-sell, and upsell optimization.

If you want, I can also recommend:

• a specific architecture
• the best tool stack
• or a model choice based on your data size and constraints