"accurate suggestion" is a correct and usable phrase in written English.
It can be used in a sentence when describing a helpful and precise recommendation or idea. For example: "Thank you for your accurate suggestion on how to improve our sales strategy. Your insight was very helpful in achieving our goals." In this example, the phrase "accurate suggestion" is used to describe a specific and exact recommendation that was effective in achieving a goal.
To generate valid and accurate suggestions, researchers have proposed integrating semantic features of the data in an ontology into the recommendation process.
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.
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.
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.
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.
It's more like autocomplete, which offers increasingly accurate suggestions depending on the information you provide.
The system proved effective, making very accurate label suggestions, even when the number of suggestions for each photo was limited to five names, and even when only a small subset of the photos was annotated.
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.
By offering accurate product suggestions online, Amazon became a reliable advisor.
More accurate friend suggestions could increase engagement, time on site, and interconnections between users that make Tagged sticky.
