Throughout the evolution of modern search bots and engines, the concept of the filter bubble is ideally utilized by companies to give users recommendations almost similar to what has been searched before or even purchased by an individual.
Although this might seem a very tactical advertisement scheme, it brings a significant issue: the lack of exploration of various options and repeatedly pushing the user into the loop of the same results.
To escape this endless loop of repeated recommendations, a group of engineers developed an algorithm named Pyrorank that gives a broader array of recommendations by decreasing the impact of the user profiles on the search engine while maintaining essential and diverse results.
The full description of the technical mechanism that works is written in the Advance in Swarm Intelligence as a conference paper. Anasse Bari, a clinical associate professor at NYU’s Courant Institute of Mathematical Science and the co-founder of Pyrorank, associates nature as the true inspiration to output solutions for complex computer science problems. Professor Anasse believes that by living in accordance with natural phenomena, nature can give the person simple yet optimal solutions for complex problems.
She also believes that the traditional recommendation algorithms are biased as they show almost identical results to users similar to you or what you, the person, have bought. An example of this mechanism is an Apple product user, who will see more and more Apple product recommendations if they have already bought something from Apple.
The biases of existing recommendation algorithms are highly concerning, particularly in the political discourse community, where new content is shown that is similar to the ideas of the past. Such doings of carried-on bias and pre-existing concepts in domains like politics will defy the notion of political growth and openness to new ways of governance.
Therefore to provide a solution to such problems by minimizing the repeated loop of recommendations of identical interactions and purchased products, Pyrorank was made. To make things more neutral and less biased, Pyrorank works on the feature of Add-on function that simply adds more new and fresh content to the recommenders system.
Pyrorank is a significant breakthrough in the world of several recommendation systems because the underlying algorithm of Pyrorank promotes diversification and prevents the wastage of many essential engineering hours.
A comparison was made between Pyrorank and the traditional recommendation system to test the viability of each system. This experiment was carried out on large Movielens, Good Books, and Goodreads datasets. The objective of the testing was to find out which system stays true to the purpose of accurate recommendations while simultaneously providing diversification and unrepeated results.
The results were hugely in favor of Pyrorank, which not only stuck to genuine core recommendations but also gave mixed results that did not align with the past results of product purchases or to someone who is a similar user.
Read next: Only 40% of People Can Identify Bots from Humans
Although this might seem a very tactical advertisement scheme, it brings a significant issue: the lack of exploration of various options and repeatedly pushing the user into the loop of the same results.
To escape this endless loop of repeated recommendations, a group of engineers developed an algorithm named Pyrorank that gives a broader array of recommendations by decreasing the impact of the user profiles on the search engine while maintaining essential and diverse results.
The full description of the technical mechanism that works is written in the Advance in Swarm Intelligence as a conference paper. Anasse Bari, a clinical associate professor at NYU’s Courant Institute of Mathematical Science and the co-founder of Pyrorank, associates nature as the true inspiration to output solutions for complex computer science problems. Professor Anasse believes that by living in accordance with natural phenomena, nature can give the person simple yet optimal solutions for complex problems.
She also believes that the traditional recommendation algorithms are biased as they show almost identical results to users similar to you or what you, the person, have bought. An example of this mechanism is an Apple product user, who will see more and more Apple product recommendations if they have already bought something from Apple.
The biases of existing recommendation algorithms are highly concerning, particularly in the political discourse community, where new content is shown that is similar to the ideas of the past. Such doings of carried-on bias and pre-existing concepts in domains like politics will defy the notion of political growth and openness to new ways of governance.
Therefore to provide a solution to such problems by minimizing the repeated loop of recommendations of identical interactions and purchased products, Pyrorank was made. To make things more neutral and less biased, Pyrorank works on the feature of Add-on function that simply adds more new and fresh content to the recommenders system.
Pyrorank is a significant breakthrough in the world of several recommendation systems because the underlying algorithm of Pyrorank promotes diversification and prevents the wastage of many essential engineering hours.
A comparison was made between Pyrorank and the traditional recommendation system to test the viability of each system. This experiment was carried out on large Movielens, Good Books, and Goodreads datasets. The objective of the testing was to find out which system stays true to the purpose of accurate recommendations while simultaneously providing diversification and unrepeated results.
The results were hugely in favor of Pyrorank, which not only stuck to genuine core recommendations but also gave mixed results that did not align with the past results of product purchases or to someone who is a similar user.
Read next: Only 40% of People Can Identify Bots from Humans
