"accurate suggestions" is a correct and commonly used phrase in written English.
It refers to helpful or useful ideas or recommendations that are precise and correct. Example: The consultant provided us with accurate suggestions on how to improve our sales strategy.
In your own city, Facebook's plentiful data provides accurate suggestions of what to check out, but it could also be a big help when you're travelling.
To generate valid and accurate suggestions, researchers have proposed integrating semantic features of the data in an ontology into the recommendation process.
The startup has built its recommendation engine using machine learning algorithms, so that as the platform grows, it takes activity and user preferences to fine-tune product-to-product recommendations, making for more accurate suggestions for individual shoppers.
Covet's co-founder Munjal Shah says that the site will soon let users indicate if the suggested items fit their style, and Covet will use this knowledge to make more accurate suggestions in the future.
These group your friends automatically based on criteria like location and employer — and they also make it very easy to build your own custom lists, providing uncannily accurate suggestions that makes the list-building process far less tedious.
It's more like autocomplete, which offers increasingly accurate suggestions depending on the information you provide.
Along these lines, we had @rockskimmer's suggestion of Ryan Stiles as Bill Nye, @iamjoelwayne's suggestion of Chris Cooper as Robert Oppenheimer, and @MariusSchrader's entirely obscure but eerily accurate suggestion of Ezra Miller as the Roman emperor Elagabalus.
Suddenly, socially-produced content recommendations seem a very blunt instrument to accomplish a very subtle goal, and this has left the floor open for more accurate, personalised suggestions to come from a different source - algorithmic engines.
More accurate friend suggestions could increase engagement, time on site, and interconnections between users that make Tagged sticky.
This confirms our findings described in [53], where we have reached a speedup of factor 3 already with moderately accurate annotation suggestions.
Airbnb is also relatively transparent about how it collects and analyzes user data, and often puts it to use in ways that increase users' control over how they use the platform — for instance, to generate more accurate pricing suggestions for hosts.
