How We Rank

Cherry Picks is powered by AI Technology with CherryRatings-AI and CherryPicks-AI.

While surfing the internet for the products you need could be time-consuming, making a decision on what type of product to buy is another huge task especially if you are inexperienced. Without any stress, Our product ranking is based on a systematic method of collecting reviews and the use of computer software to reach rank them. This process includes review collation, review sorting and buffering, merging and presentation, product rating, and lastly the hierarchical order of products.

Here is our ranking mechanics,

1. Products and review collection

We use our legitimate web scraper that utilizes our in-house CherryPicks-AI rating system. The CherryPicks AI uses Tensorflow to choose the best products in a certain market category. We then collect the reviews for each of the products from reliable data sources, such as Amazon, Walmart, and so on. The regularity of this process with the help of web scraping tools that suggests the recency of our reviews, We use a combination of a platform-specific web crawler to scrape fresh data, and Experiencing products firsthand is an effective way to maintain a fresh collection.

2. Review sorting and buffering

With our buffer system, reviews are sorted and collated based on the algorithm of a predetermined specification. We use the BART framework and algorithm to sort the reviews and get rid of the false reviews so that our verdicts won’t be contaminated. BART is a machine learning model that allows us to group reviews by the type of review they are. It is usually used on Amazon reviews, it helps the company to make sure that their review policy is followed and they can provide a better experience for customers.

One of our most important tasks is filtering out bad reviews. Our algorithm is very rule-based and analyzes and evaluates every single sentence from the scraped reviews. It then checks these against texts from reviews we know are reliable and ruthlessly removes the non-conforming ones.

3. Merging and presentation by CherryRatings-AI

After the reviews have been sorted, they are merged in a way to achieve a presentable review catalog with brevity based on content and context.

Now CherryRating-AI is coming which using the BART framework, CherryRating-AI is an algorithm that helps us assign scores to positive and negative customer experiences around similar topics. CherryRating-AI's algorithm assigns scores to positive and negative reviews, based on how much they agree with one another. This will help companies create better customer experiences.

There, we arrived at an opposing group of reviews (perfect or fair). This will assist you in making informed decisions on products to purchase without having to spend much time surfing the net, It can predict which reviews are likely to be positive or negative and help companies get rid of fake reviews.

4. Product Ranked by CherryPicks-AI

To get the best results, We developed an in-house CherryPicks-AI rating system that uses the TensorFlow framework to generate the best list. Excerpt CherryRatings-AI, which mainly evaluates customer review content, The score of items is based on over ten parameters such as analyzing customer review content, Seller feedbacks score, product reviewing rating statistics, brand reputation evaluation, comparing product price with features, etc.

Products are scored based on the weight assigned to the reviews, seller feedback, brand reputation, and so on based on users. Either perfect or fair reports and comments from consumers, there are specific weights the AI machine allot to each. Weights are summed and rated on average to arrive at a score. 

5. The hierarchical order of products

The final step is evaluating and comparing the highest-scoring products based on user needs. A side-by-side product evaluation based on the aggregated weight and market occupancy is done with demand also put into consideration. This is used in the ranking of the products in that category. No doubt, this will give room for a thorough ranking of existing and market-dominating products without ruling out space for new inventions that might also have achieved some good consumer feedback.

If you have any questions or feedback on our product rankings, please contact the technical department directly. 

E-mail of technical department,

Last Updated: May 27,2022