How Does Collaborative Filtering Use Explicit Feedback?
Collaborative filtering can be used with explicit feedback (user-inputted data such as a person’s 4-star rating of a movie on Netflix), implicit feedback (inferred data that a user was interested in a TV series if they watched one episode after another), or a combination of the two.
One example of how explicit feedback can be used in collaborative filtering is with movie ratings (1-star, 2-star, etc.). An algorithm takes all the ratings across all customers to make a big table or matrix. Then, the algorithm uses matrix factorization as a mathematical way to represent information about the users and items.
These representations can then be used to compute theoretical ratings for movies that an individual may not have seen before. In the end, this manifests as “people who purchased winter boots also purchased cold medicine.”
This approach differs from content-based filtering mentioned above in that the recommended item is not suggested because it is necessarily similar to the original item, but because patterns of human behavior suggest commonality.
Can Collaborative Filtering Be Used With Implicit Feedback?
Continuing the rating example from above, how do users that don’t bother to rate movies or products still receive valuable recommendations? While explicit ratings are great, they are also rare. Therefore, companies may use the collaborative filtering technique with implicit ratings and feedback, as well.
Implicit feedback refers to the idea that you can infer what a person thinks about a product based on their behavior. Engagements like clicking on an item, time spent on a product page, purchasing an item, watching five minutes of an episode, or binging the entire season form the basis of implicit ratings.
Mathematically transforming information about how a person interacts with a product can serve as a stand-in for ratings, although with some assumptions baked in. For example, if someone bought an item, the assumption is that they must like the item. Or if someone watches three seasons of a show, the assumption is not that they fell asleep with auto-play on. While these assumptions may not always be accurate, with the large volumes of data common in streaming media or e-commerce, clear trends still emerge.
What Are the Risks or Biases Associated With Using Recommender Systems?
One big risk is that by using like to recommend like, suggestions can fall into a silo. At best, this causes recommendations to be boring. At worst, recommendations can reflect social bias or discrimination present in the underlying dataset.
Siloing can be due to limitations in content-based filtering or due to the predictability and stereotypy of human behavior. People who watch one horror movie probably watch multiple horror movies. Pretty soon, the algorithms are only recommending horror movies.
Human bias can also be reflected if the system’s AI strategy is not carefully considered. For example, using a recommender system without additional modification to suggest college classes may recommend engineering classes to male students and early childhood education classes to female students—based on gender-biased enrollment patterns.
That said, there are statistical and mathematical steps one can build into the AI design to avoid pigeonholing. A truly effective and responsible recommender system involves a component to identify and address bias and siloing.
Ultimately, the next time you are wondering how Amazon knows what shoes you like, or Netflix plans the perfect Friday evening, you have a recommender system to thank.
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Editor’s note: This post was originally published in 2020 and has been updated and republished.