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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Karthik Sridharan
DESCRIPTION:Title: Towards Building Non-polarizing Recommender
	 Systems\nSpeaker: Karthik Sridharan\nAbstract: An inherent trait of
	 recommendation systems is that they tend to influence their users. Often
	 this influence is unintentional and sometimes causes polarization of the
	 users. Consider a social media agency interested in recommending new
	 articles to its users over multiple days. If the agency tries to simply
	 predict what the user might like and greedily provide recommendations\,
	 it might end up polarizing its users. To better illustrate this
	 phenomenon\, consider the news agency that provides articles or
	 recommendation about fruits. Say we have a user who initially likes
	 apples and oranges equally and just happens to receive some article
	 about apples and indicates to the system that the she might like apples.
	  The recommender system that learns of this will initially start to
	 recommend with a mild bias\, articles about apples and their health
	 benefits. Now subsequent rounds of interactions with this system leaves
	 on the user a strong opinion about apples and the user might start to
	 prefer apples over oranges\, all the while the system further would
	 strengthen its belief that the user really prefers apples over oranges.
	 Continuous interaction with such a system leaves this user\, who started
	 as a person initially being neutral about apples Vs oranges\, into
	 someone who is an apple fanatic. Clearly this was just by happenstance
	 and just as easily\, the initial interactions could have swayed towards
	 user liking oranges. The issue of polarization is further worsened by
	 the notion of confirmation bias of users who perceive contents
	 differently based on their prior beliefs on each round which might
	 further speed up the polarization. Additionally\, the issue of
	 polarization by recommender systems can be worsened when one considers
	 the fact that the users might be part of a social network and tend to
	 share ideas and opinions. Users are often part of user groups or
	 cliques\, and these groups tend to further influence user preferences
	 within the group. Specifically\, there is an intrinsic bias for users to
	 follow the herd\, so to speak\, and users can be more easily convinced
	 to agree with their group's view while disagreeing strongly with others
	 not in the group. Hence\, a recommendation system\, by making the greedy
	 choice of articles to show to the users\, might inadvertently polarize
	 its users intro groups with strongly opposing opinions on issues. \n
	 \nIn this talk I will present some of our initial attempts at building
	 theory and algorithm design principles for building machine learning
	 systems that not only aim to predict or recommend with high accuracy but
	 also aim to no further polarize its users. Specifically\,  we assume
	 that the users of the system are interconnected to each other via a
	 social network and that the machine learning algorithm has access to the
	 structure of this social network. We then build and extend existing
	 mathematical models for formation/evolution of opinions of users based
	 on whats recommended to them and the interaction with their friends in
	 the social network. Finally we provide an algorithm design principle for
	 building recommendation systems that use the knowledge of the underlying
	 social network to provide recommendations to users that not only aim for
	 high accuracy but simultaneously aim to reduce a natural measure of
	 polarization we propose. We show that under our model of opinion
	 formation dynamics (that subsumes existing model for opinion dynamics)
	 our recommendation algorithm provably has low polarization effect.
	 \n\n\nJoint work with Wilson Yoo
LOCATION:Gates 122
UID:2019-04-16
STATUS:TENTATIVE
DTSTART:20190416T160000Z
DTEND:20190416T170000Z
LAST-MODIFIED:20190416T154149Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T121911Z
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