AI-recommendations
We implement AI recommendations: ML algorithms that suggest relevant products, content or actions to the user based on their and others' behavior. To raise relevance and engagement. Honestly upfront: AI recommendations require a sufficient volume of quality data (on small data and for new users — a 'cold start', they work poorly); they GUESS, they do not know precisely — they make mistakes and sometimes produce odd results; they require privacy/consent and constant monitoring; they can create a bubble; and it is not a sales/retention guarantee. For a small catalog ML is often excessive — we will honestly assess.
AI-recommendations — overview
AI recommendations are machine-learning recommendation algorithms suggesting relevant items (products, content, actions) to the user based on patterns in data: their history, the behavior of similar users, item properties. It is a technically more complex level than manual personalization (993). Honestly about data, this is the foundation: the quality of AI recommendations directly depends on the volume and quality of data. Little data — weak recommendations; 'dirty' data — garbage out. For a new user or a new product there is almost nothing to recommend (a 'cold start') — a known limitation to work around (not ignore). We honestly say: ML is about data, not magic. Honestly about 'guess, not know': recommendation models find statistical patterns and predict the likely but do not understand the user and make mistakes. Sometimes they produce odd or irrelevant suggestions (especially at the start). It is a probabilistic tool that raises the chances of relevance, not an oracle. Honestly about privacy and monitoring: recommendations on behavioral data require consent and responsible handling; plus the model must be monitored and maintained (patterns change, quality can degrade). It is not 'set up and forget'. Honestly about the bubble and bias: recommendations can lock the user in a narrow bubble and amplify data skews (recommend only the popular). A good system accounts for this (diversity, control). Honestly about feasibility: for a small catalog or low traffic full ML is often excessive — rules/manual personalization (993) are simpler and more reliable. We will honestly assess whether you specifically need ML. Honestly about the effect: with sufficient data it raises relevance and engagement, but it is not a sales/retention guarantee. Honestly about access: data, consent, a resource for maintenance/monitoring are needed. An important boundary: this is AI recommendations (ML); manual personalization — 993; AI product features — the AI section. Picture this: instead of 'the same listing for everyone' — relevant ML suggestions, honestly built on data and monitored. The base price starts from 60,000 ₽ (depends on data and integration).
Problems we solve
- Users are shown the same thing, without relevant suggestions.
- Data exists but is not used for smart recommendations.
- Past recommendations produced odd and irrelevant results.
- Unclear whether ML is justified or simple rules suffice.
What's included in the AI-recommendations service
- Implementing AI recommendations (ML) on your data
- Assessing data sufficiency and quality (including 'cold start')
- An honest assessment: is ML needed or do rules/personalization (993) suffice
- Privacy/consent + model monitoring and maintenance
- Accounting for the bubble and data bias (diversity)
- Honest boundaries (guess not know; data needed; no sales guarantee)
- A link with personalization (993)
- Handover and review with you
What you get
- Relevant recommendations based on data
- Correct handling of privacy and model monitoring
- Accounting for 'cold start' and diversity (against the bubble)
- Honest boundaries (a probabilistic tool; data needed; no guarantee)
How the work goes: steps
- We assess data and ML feasibility (vs rules/personalization)
- We implement recommendations with privacy, monitoring, diversity
- We honestly set boundaries and a maintenance 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 AI recommendations always be accurate and relevant?
No, honestly: recommendation models GUESS based on statistical patterns, they do not understand the user, and they make mistakes — sometimes producing odd results, especially at the start and on small data. It is a probabilistic tool that raises the chances of relevance, not an oracle. Plus there is a 'cold start' for new users/products. We honestly make it 'more relevant', not 'always on target'.
Does every project need AI recommendations?
No, honestly: full ML is justified with a sufficient volume of quality data and traffic. For a small catalog or low traffic it is often excessive — rules or manual personalization (993) are simpler and more reliable. Plus ML requires data, privacy and constant monitoring (not 'set up and forget'). We will honestly assess whether you specifically need ML rather than sell 'AI' for hype.
Set up recommendations — and it all works by itself?
No, honestly: the model must be monitored and maintained — behavior patterns change, quality can degrade, skews are possible (recommends only the popular, locks in a bubble). A good system accounts for diversity and quality control. Plus consent and privacy for data are needed. It is a living process with maintenance, not a one-off setup — we honestly build this in.
About the provider
The «AI-recommendations» service is provided by PDV Expert — a team specialising in «Customer retention». We work under contract and deliver a written report with recommendations.