Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Fair recommendation through conditioning on systematic presentation in recommender systems

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dc.contributor Graduate Program in Computational Science and Engineering.
dc.contributor.advisor Baydoğan, Mustafa Gökçe.
dc.contributor.author Bozal, Özge.
dc.date.accessioned 2023-03-16T10:05:22Z
dc.date.available 2023-03-16T10:05:22Z
dc.date.issued 2021.
dc.identifier.other CSE 2021 B78
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12451
dc.description.abstract Recommendation engines are interactive systems aiming to predict users’ top preferences according to their choices. A limitation of the recommender systems is that over time the recommendations get narrower in scope. That means the popular items are favored and the less frequently visited ones are censored. Consequently, users are prevented from exploring new alternatives. The so-called filter bubble is the inevitable outcome of the self-reinforcing feedback loops where the preference estimations depend on the user choices. Self-reinforcing feedback loops are both cause and e↵ect of over- and under presentation of some content in interactive recommender systems. That results in inaccurate user preference estimates, namely, overestimation of over-presented con tent and vice versa. The burden is on the recommender system to eliminate the self reinforcing feedback loops for a more accurate preference estimation. In this regard, we define the “fairness” criteria for an interactive recommender system considering the adverse impacts of these self-reinforcing feedback loops. We also claim that designing an intelligent presentation mechanism is essential to meet those criteria. To prove the claim we address two models that explicitly incorporate or ignore the systematic and limited exposure to alternatives. By simulating real-world biases, we demonstrate that ignoring systematic presentations results in overestimation of promoted options and underestimation of censored alternatives. Simply conditioning on the limited exposure is a remedy for these biases.
dc.format.extent 30 cm.
dc.publisher Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2021.
dc.subject.lcsh Expert systems (Computer science)
dc.subject.lcsh Intelligent control systems.
dc.subject.lcsh Artificial intelligence.
dc.title Fair recommendation through conditioning on systematic presentation in recommender systems
dc.format.pages xi, 42 leaves ;


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