With RankAnything, users can rank anything and find rankings of everything. The best part is that the system learns about each user based on the rankings they submitted and hence provide them with rankings that are more and more relevant to each user over time.
People look for rankings everywhere. They rank and look for rankings when they shop, watch a movie, find classes, etc. However, all the rankings are scattered all over the internet. And the rankings are not tailored to each user. Some people like Apple products. Others prefer Android. Obviously, they have different preferences when it comes to other things. With RankAnything, people share rankings about everything. RankAnything aggregates and delivers "customized" better rankings by taking into account of each user's preference.
We use a proprietary algorithm to understand each user's preference based on the rankings they submitted. For example, John submitted a list of his top 10 movies and restaurants in Chicago. The system extracts his personal preference by mining extensive user ranking DB. Now when John looks up for rankings books to read over the weekend, he will find a list of top 10 books which is tailored to him and him only. Over time, the system better understands (i.e. continuously learn from users behaviors) and hence produces better and more relevant rankings. Now John can use RankAnything to discover products he wouldn't have known otherwise and get suggestions on movies that he might have missed.
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