Making Sense of Recommendations

Michael  Yeomans; Anuj  Shah; Sendhil  Mullainathan; Jon  Kleinberg

Article | Journal of Behavioral Decision Making | October 2019

Making Sense of Recommendations

by Michael Yeomans, Anuj Shah, Sendhil Mullainathan and Jon Kleinberg

Abstract

Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Although people frequently encounter these algorithms because they are cheap to scale, we do not know how they compare to human judgment. Here, we compare computer recommender systems to human recommenders in a domain that affords humans many advantages: predicting which jokes people will find funny. We find that recommender systems outperform humans, whether strangers, friends, or family. Yet people are averse to relying on these recommender systems. This aversion partly stems from the fact that people believe the human recommendation process is easier to understand. It is not enough for recommender systems to be accurate, they must also be understood.

Keywords: recommender systems; artificial intelligence; interpretability; Information Technology; Forecasting and Prediction; Decision Making; Attitudes;

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Yeomans, Michael, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. "Making Sense of Recommendations." Journal of Behavioral Decision Making 32, no. 4 (October 2019): 403–414.

Article | Journal of Personality and Social Psychology | September 2017

It Doesn't Hurt to Ask: Question-asking Increases Liking

K. Huang, M. Yeomans, A.W. Brooks, J. Minson and F. Gino

Conversation is a fundamental human experience, one that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational behavior: question-asking. Across three studies of live dyadic conversations, we identify a robust and consistent relationship between question-asking and liking: people who ask more questions are better liked by their conversation partners. When people are instructed to ask more questions, they are perceived as higher in responsiveness, an interpersonal construct that captures listening, understanding, validation, and care. We measure responsiveness with an attitudinal measure from previous research as well as a novel behavioral measure: the number of follow-up questions one asks. In both cases, responsiveness explains the effect of question-asking on liking. In addition to analyzing live get-to-know-you conversations online, we also studied face-to-face speed-dating conversations. We find that speed daters who ask more questions during their dates are more likely to elicit agreement for second dates from their partners, a behavioral indicator of liking. We trained a natural language processing algorithm as a “follow-up question detector” that we applied to our speed-dating data (and can be applied to any text data to more deeply understand question-asking dynamics). The follow-up question rate established by the algorithm explained why question-asking led to speed-dating success. We also find that, despite the persistent and beneficial effects of asking questions, people do not anticipate that question-asking increases interpersonal liking.

Keywords: question-asking; liking; responsiveness; Conversation; natural language processing; Interpersonal Communication; Behavior;

Citation:

Huang, K., M. Yeomans, A.W. Brooks, J. Minson, and F. Gino. "It Doesn't Hurt to Ask: Question-asking Increases Liking." Journal of Personality and Social Psychology 113, no. 3 (September 2017): 430–452. View Details

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When entering into a negotiation, individuals have the choice to enact a variety of communication styles. We test the differential impact of being “warm and friendly” versus “tough and firm” in a distributive negotiation, when first offers are held constant and concession patterns are tracked. We train a natural language processing algorithm to precisely quantify the difference between how people enact warm versus tough communication styles. We find that the two styles differ primarily in length and their expressions of politeness (Study 1). Negotiators with a tough communication style achieved better economic outcomes than negotiators with a warm communication style, both in a field experiment (Study 2) and in a laboratory experiment (Study 3). This was driven by the fact that offers delivered in tough language elicited more favorable counteroffers. We further find that the counterparts of warm versus tough negotiators did not report different levels of satisfaction or enjoyment of their interactions (Study 3). Finally, in Study 4 we document that individuals’ lay beliefs are in direct opposition to our findings: participants believe that authors of warmly worded negotiation offers will be better liked and will achieve better economic outcomes.

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Making Sense of Recommendations

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