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DL Seminar | Personalized Recommender Systems: Technological Impact and Concerns

Updated: 6 days ago


Individual reflections by Daphne Na and Yan Ji (scroll below).



By Daphne Na

Cornell Tech


Is Technology the Answer?


Whether we are aware of it or not, our daily activities are highly influenced by personalized recommender systems. When we are tying to find a movie to watch on Netflix or browsing through Amazon for a new hand soap to buy, personalized recommender systems are in the works to provide us with the best match that is likely to make us click that continue watching or buy button. The systems look very convenient at first, but I cannot stop wondering – “how are these systems so eerily accurate?” and “what are some alarming adverse impacts and how do we address them?”


Amy Zhang’s presentation on this topic in the Digital Life Research Seminar provided some interesting insights on how our increasingly automated society can deal with problems generated by personalized recommender systems and discussed alternative approaches to addressing these issues. As a PhD Candidate in Operations Research and Information Engineering at Cornell Tech, her research focuses on “approximating large systems by a small number of clusters in settings that can be modeled as Markov Decision Processes (MDP).” Amy applied her research to analyze personalized recommendations in depth.


How do they work?


Content based filtering

The fundamental motivation of personalized recommendations is to filter the content to be more relevant for each individual. The systems’ algorithms make inferences from both direct (ratings, feedback, etc) and indirect (purchase history, access patterns, etc) signals. The goal is to predict the relevance (signal) of a new item for a given user. Based on the data from direct and indirect signals, the content-based filtering systems work to produce outputs that are similar to the user’s previous choices.


Collaborative filtering

Collaborative filtering works with the similarities among users and makes use of quality judgment from others to recommend relevant products. The content-based filtering utilizes User A’s information such as what products they have seen, clicked on, or purchased before …etc to analyze if they would be similar to User B’s behaviors. With collaborative filtering, the system would recommend User B similar items that User A has shown some interests in.


Amy suggests that the current systems are not perfect and there are issues with current filtering methods for personalized recommender systems. For example, the algorithms can only make recommendations based on the already existing preferences of the user. This can result in limiting the space that the users may be interested in. Recommendations may be inaccurate, leading the users to spend more time figuring out what they actually want on their websites. Furthermore, there are privacy issues in utilizing users’ information to feed into personalized recommender systems.


Amy leaves the presentation with an important question to ask ourselves – can technology be the answer to this problem? At the end of the day, it is us, humans, that design these algorithms.


By Yan Ji

Cornell Tech


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