Learning resource suggestions
Recommend suitable material using the learner’s current topic, chosen goals and relevant prerequisites. Explain the connection and avoid treating inferred preferences as a reliable assessment of ability.
Data & decision intelligence
AI recommendation systems
Deliver relevant suggestions for customers, learners and teams based on context and data.
Built around a specific business need.
A useful recommendation helps someone choose from options that are relevant to their situation. Popularity alone may not be enough: a learner may need an earlier topic, a customer may have specific constraints and an employee may require a document that is rarely used but highly applicable.
We design recommendation systems around the decision being supported and the evidence available. Business rules, contextual matching and learned ranking can each play a role. The approach makes eligibility, user control and evaluation explicit, avoiding the assumption that more clicks automatically represent a better experience.
Who this is for
- Learning providers guiding users towards appropriate content or pathways.
- Businesses helping customers navigate relevant products or services.
- Knowledge teams improving discovery across large information collections.
Where this can make a practical difference.
Product or service discovery
Rank available options within the customer’s stated requirements and commercial constraints. Keep eligibility rules separate from ranking so an appealing suggestion is not presented when it cannot be supplied.
Related knowledge retrieval
Help staff discover useful documents, examples or next steps connected to their current task. Respect document permissions and freshness rather than recommending content merely because it was frequently opened.
How we approach the work.
Define relevance and eligibility
Agree what makes a recommendation useful and which options must be excluded. Identify where users should provide explicit preferences instead of having the system infer them.
Inspect available signals
Review item descriptions, interaction history and contextual data. Distinguish genuine preference from exposure, compulsory activity or the limited options users were previously shown.
Build a useful baseline
Start with transparent rules or content matching where appropriate. Compare more complex ranking methods against that baseline rather than assuming personalisation always adds value.
Evaluate the experience
Assess relevance, coverage and unsuitable suggestions using reviewed examples. Where live experimentation is appropriate, agree outcome measures and safeguards before changing what users see.
Monitor feedback and control
Let users dismiss or correct recommendations where useful. Review stale items, repeated suggestions and changes in behaviour, retaining a fallback for new users or sparse data.
The sequence is tailored to the agreed scope, dependencies and risk. It describes our delivery approach, not a fixed timeline or performance guarantee.
What the engagement can deliver.
- Relevance objectives and eligibility rules.
- Item catalogue structure and signal assessment.
- Baseline and selected recommendation approach.
- Recommendation interface with appropriate explanations and controls.
- Evaluation, monitoring and catalogue-maintenance guidance.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
Relevant recommendation rate
Assess recommendations against reviewed user needs or appropriate outcome signals, not clicks alone.
Catalogue coverage
Examine whether useful eligible items are discoverable rather than repeatedly favouring a narrow popular subset.
Unsuitable suggestion rate
Track recommendations that breach prerequisites, availability, permissions or other agreed constraints.
These are measures to discuss and baseline. Targets are agreed for the specific system and data; no universal improvement percentage is assumed.
What to prepare for discovery.
- A defined item catalogue and relevant eligibility constraints.
- Examples of useful and unsuitable recommendations.
- Permission to use the selected interaction or contextual information.
Questions worth asking.
Can we start without a large user history?
Yes. Content-based matching, explicit preferences and clear rules can provide a starting point. The appropriate approach depends on the catalogue and decision, not simply the volume of tracking data.
Will recommendations create a narrow feedback loop?
That is a design risk to examine. We can assess coverage, repetition and the relationship between exposure and feedback, then include appropriate variety and user control within the agreed objective.
How do you decide whether a recommendation worked?
We define the intended outcome first. A purchase, useful learning interaction or successful information search requires different evidence; a click is usually only one part of that assessment.