Sentence examples for robust recommendations from inspiring English sources

"robust recommendations" is a grammatically correct phrase and can be used in written English.
It is typically used to describe strong or reliable suggestions or advice. Example: The expert panel provided robust recommendations for improving the company's financial strategy.

Exact(19)

So let us take the most bizarre result of Sir Christopher's robust recommendations.

The nature of qualitative research precludes robust recommendations for policy.

Prof Alan Miller, chairman of the Scottish Human Rights Commission, said he was extremely pleased with the committee's "robust recommendations".

Ministers are braced for some robust recommendations – concerning both institutional failures to prevent cultural cantonisation and (for instance) the persistent problem of Islamic radicalisation.

Many of her robust recommendations, therefore, are important and timely: a new programme to help community cohesion including the promotion of English language and measures to tackle exclusion, inequality and segregation in schooling.

In response to frequent concerns from foster carers, the review also makes robust recommendations around birth family contact, which often goes ahead against the advice of carers and despite clear evidence of distress to the child.

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Similar(41)

"That sort of thing happened fairly frequently: you'd make a very robust recommendation and it would get scaled back.

Netflix is a great example: Without granting access to their personal data, customers would not benefit from Netflix's robust recommendation engine, which is a powerful way for consumers to discover new content that is highly relevant to their interests.

I've watched the product evolve from a simple check-in service to a robust recommendation engine.

Chen hopes to eventually add more robust recommendation features like Songs You May Like, based on stuff your friends have listened to that you haven't heard.

Quint gave the example of Netflix and how they built a robust recommendation engine that is helping users discover new content – something that could not have happened unless users let them gather data about what they were watching and when.

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