Recommendation system
We implement a recommendation system: 'similar products', 'bought together', personal selections — so the user sees what is relevant and buys more. It works on user behavior and product attributes. Honestly upfront: recommendations are statistical models, they do NOT guarantee sales growth and they err; they need data (for a new store or new products there is a 'cold start' — almost nothing to recommend from); quality grows over time and requires setup and evaluation, not 'turn it on and sales grow'.
Recommendation system — overview
A recommendation system is a mechanism that selects relevant products/content for the user: similar items, complementary ('bought together'), personal selections based on view and purchase history. We use suitable approaches (content-based, collaborative filtering, hybrid) for your catalog and data volume. Honestly about data and the 'cold start', this is key: recommendations feed on data. If the store is new, traffic is low or a product just appeared — the system has almost nothing to build recommendations from (the cold-start problem). In such cases you start with simple rules (popular, by category) and grow personalization as data accumulates. Promising strong personalization 'from day one without data' is dishonest. Honestly about 'no sales guarantee': recommendations are a statistical tool that raises the chance of a relevant display but does NOT guarantee revenue growth. The effect depends on the catalog, traffic, data quality and the product itself; it needs verification via metrics and A/B. Honestly about errors: the model sometimes recommends irrelevant things — that is normal for statistics, we track and improve it rather than pass it off as ideal. Honestly about the 'bubble' risk: excessive personalization can narrow choice (the user sees the same things) — we balance relevance and diversity. Honestly about the effect and time: the system improves with data and iterations; it is a process, not a one-off switch. Honestly about access: product and behavior data and code access are needed. An important boundary: this is recommendations; interface/content personalization is broader — 873; semantic search — 874. Picture this: instead of 'the user does not find what else to buy' — relevant suggestions that grow in accuracy with data. The base price starts from 50,000 ₽ (depends on the catalog and data).
Problems we solve
- Users do not see relevant products and leave with one purchase.
- No 'similar' and 'bought together' — extra revenue is lost.
- Selections are static, do not account for behavior.
- The catalog is large — selections cannot be made by hand.
What's included in the Recommendation system service
- Choosing the approach (content-based/collaborative/hybrid)
- Recommendations: similar, complementary, personal selections
- Solving the 'cold start' (rules → personalization by data)
- Balancing relevance and diversity (against the 'bubble')
- Effect evaluation via metrics and A/B
- Honest boundaries (data needed; no sales guarantee)
- A link with personalization (873) and search (874)
- Handover and review with you
What you get
- Relevant recommendations (similar/complementary/personal)
- Gradual accuracy growth as data accumulates
- Extra-revenue potential (sales growth — not a guarantee)
- A balance of relevance and diversity
How the work goes: steps
- We assess the catalog, data, traffic; choose the approach; collect access
- We implement recommendations accounting for the cold start
- We measure via A/B, honestly set boundaries and an iteration plan with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will recommendations raise my sales right away?
Not guaranteed, and that is honest: recommendations are a statistical tool that raises the chance of a relevant display but does not promise revenue growth. The effect depends on the catalog, traffic, data and product, and must be verified by metrics and A/B. We raise relevance and measure the result, but promising 'turn it on — sales grew' would be dishonest.
Will it work on a new store without data?
With limitations — that is the 'cold start'. Without behavior history the system has almost nothing to build personalization from. We start with simple rules (popular, by category) and grow personalization as data accumulates. Strong personalization 'from day one without data' cannot be honestly promised — it comes with traffic and time.
Can recommendations show irrelevant things?
Yes, sometimes — that is a property of statistical models, and we honestly track it. The model can miss, especially with little data. We measure quality, improve over iterations and balance relevance with diversity (to avoid showing the same things). Perfect accuracy does not exist, but the system gets better with data.
About the provider
The «Recommendation system» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.