“Who doesn’t like dinosaurs?” Finding and Eliciting Richer Preferences for Recommendation

Schnabel, Tobias; Ramos, Gonzalo; Amershi, Saleema

doi:10.1145/3383313.3412267

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…Significantly, our results provide additional empirical support to reject the much-cited personalization-privacy trade-off in the context of rating and review-based preference information provided to recommendation systems (Schnabel et al 2020). We contend that once users have given up personally sensitive information to use platform services, general privacy concerns are no longer a predictor of providing intentions.…”

Section: ; Wang Et Al 2017)supporting

confidence: 56%

What influences users to provide explicit feedback? A case of food delivery recommenders

Haruyama

Hidaka

2022

Preprint

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Although explicit feedback such as ratings and reviews are important for recommenders, they are notoriously difficult to collect. However, beyond attributing these difficulties to user effort, we know surprisingly little about user motivations. Here, we provide a behavioral account of the sparsity problem by theorizing the possible presence of feedback loops in user-recommender interactions. Specifically, we hypothesized that poorly motivated elicitation practices, accompanied by an algorithmic shift away from explicit feedback, might be inhibiting user feedback. To better understand underlying motivations, we administered a survey to measure constructs influencing the rating and review intentions of U.S. food delivery platform users (n = 796). Our model, combining the Technology Acceptance Model and Theory of Planned Behavior, revealed that standard industry practices for feedback collection appear misaligned with key psychological influences. Most notably, rating and review intentions were most influenced by subjective norms. This means that while most systems directly request feedback in user-to-provider relationships, eliciting them through social pressures that manifest in user-to-user relationships is likely more effective. Secondly, most hypothesized dimensions of feedback’s perceived usefulness recorded insignificant effects on feedback intentions. These disassociations provided clues for practitioners to improve perceptions through contextualized messaging. In addition, perceived pressure and users’ high stated ability to provide feedback recorded insignificant effects, suggesting that frequent feedback requests may be ineffective. Lastly, privacy concerns recorded insignificant effects, hinting that the personalization-privacy paradox might not apply to ratings and reviews. Our results provide a novel behavioral perspective to improve feedback collection in food delivery and beyond.

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Section: ; Wang Et Al 2017)supporting

confidence: 56%

What influences users to provide explicit feedback? A case of food delivery recommenders

Haruyama

Hidaka

2022

Preprint

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“…Therefore, analogical search engines should help to reduce the cognitive effort required in the process, for example by proactively retrieving results that are 'usefully' misaligned such that searchers can better recognize (as opposed to having to recall) salient constraints and refine their problem representation. This process is deeply exploratory [93,115,118] in nature, and suggest the importance of both providing end-users a sense of progress over time [103] as well as adequate feedback mechanisms for the machine to adjust according to the changing end-user search intent [72,95,96]. For example, while the machine may 'correctly' recognize a significant anaogical relevance at a higher level of purpose representation and recommend macro-scale mechanisms to a scientist who studies nano-scale phenomena (P1 Study 1 ) or solid and semiconductor-based cooling mechanisms to a scientist in liquid and evaporative cooling systems (P3 Study 1 ), these analogs may be critically misaligned on the specific constraints of the problem (i.e.…”

Section: Support Purpose Representation At Different Levels Of Abstra...mentioning

confidence: 99%

Augmenting Scientific Creativity with an Analogical Search Engine

Kang

Qian

Hope

et al. 2022

ACM Trans. Comput.-Hum. Interact.

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Analogies have been central to creative problem-solving throughout the history of science and technology. As the number of scientific papers continues to increase exponentially, there is a growing opportunity for finding diverse solutions to existing problems. However, realizing this potential requires the development of a means for searching through a large corpus that goes beyond surface matches and simple keywords. Here we contribute the first end-to-end system for analogical search on scientific papers and evaluate its effectiveness with scientists’ own problems. Using a human-in-the-loop AI system as a probe we find that our system facilitates creative ideation, and that ideation success is mediated by an intermediate level of matching on the problem abstraction (i.e., high versus low). We also demonstrate a fully automated AI search engine that achieves a similar accuracy with the human-in-the-loop system. We conclude with design implications for enabling automated analogical inspiration engines to accelerate scientific innovation.

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“…In order to build a user model, we can ask the user to rate a selection of items [51] or to fill up a questionnaire. A significant challenge in this approach is that users are often reluctant to participate in a query process [52]; thus, the explicit collection of information is essentially a trade-off between time and effort from users and the richness of the recommendations that they will be provided [53].…”

Section: Challengesmentioning

confidence: 99%

Content and Other Resources Recommendations for Individuals with Intellectual Disability: A Review

et al. 2022

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In this review paper, we look into how a recommendation system can be adapted to and support people with intellectual disability (ID). We start by reviewing and comparing the main classes of techniques for general-purpose content recommendation. Then, centering on individuals with ID, we collect information on their special needs that may be relevant to or affected by content recommendation tasks. We review the few existing recommendation systems specifically designed or adapted to the needs of this population and finally, based on the reviewed literature sources, we catalog the traits that a future content recommendation system should have in order to respond well to the identified special needs. We hope this listing of desirable traits and future directions in our concluding sections will stimulate research towards opening the doors to the digital world for individuals with ID.

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“Who doesn’t like dinosaurs?” Finding and Eliciting Richer Preferences for Recommendation

Cited by 9 publications

References 29 publications

What influences users to provide explicit feedback? A case of food delivery recommenders

What influences users to provide explicit feedback? A case of food delivery recommenders

Augmenting Scientific Creativity with an Analogical Search Engine

Content and Other Resources Recommendations for Individuals with Intellectual Disability: A Review

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