People are accustomed to using at least one of the different networking, entertainment, marketing, and social platforms. After a period of using these sites, it is common for suggestions based on the individual users to appear automatically. This is accomplished by matching algorithms that read a block of records from a data source in a reference match. It is one of the most particular AI and deep learning applications employed by e-service platforms since it significantly improves the user experience. However, since every concept has flaws, this UCLA Anderson Review article discusses how matching algorithms work as well as some of its major drawbacks.
The article opens with the example of Netflix, which previously had more demand for newly released movies than it had the required DVDs to ship out. To counteract this issue, its recommendation system was critical in directing clients to older films where more DVDs were available. According to the article, recommender system algorithms function by anticipating how a person would rank a specific item, which is frequently based on a comparable user’s history on the site. It then presents the user with the things with the highest ratings. However, according to the article, one algorithm may not necessarily meet all criteria. Matching algorithms function by recommending things that best fit either the user’s description or the user’s desire. Sometimes, the item for which demand has been raised is not available. In this instance, matching algorithms come in handy and recommend items that are comparable to what is required.
According to the article, if the algorithm does not alter its calculations for matches made by users from external websites, it will fail to send its internal traffic to where it is most needed. As a result of this external traffic, the recommendation system may perform worse. According to the article, once users arrive at a website, matching algorithms must make choices without knowing if the next visitor is from external or internal traffic. The article suggests that the algorithm is not set up to drive traffic to a sold-out item or an organization that has already filled all of its volunteer opportunities. Doing so would simply result in dissatisfied visitors. According to the article, a modest modification of withdrawing one sign-up instead of adding affects the percentage of remaining capacity for the opportunity, and the researchers discovered that this alteration leads to improved performance promises. Finally, the article advises that balancing simplicity and economy is always crucial, but particularly so in the charity sector, where data engineering can be prohibitively expensive. This might involve marketing efforts and email recommendations that steer customers to things with a large amount of remaining capacity.