NDLI: Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources

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Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources

Content Provider	ACM Digital Library
Author	Barbu, Catalin-Mihai
Abstract	Current recommender systems mostly do not take into account as well as they might the wealth of information available in social media, thus preventing the user from obtaining a broad and reliable overview of different opinions and ratings on a product. Furthermore, there is a lack of user control over the recommendation process-which is mostly fully automated and does not allow the user to influence the sources and mechanisms by which recommendations are produced-as well as over the presentation of recommended items. Consequently, recommendations are often not transparent to the user, are considered to be less trustworthy, or do not meet the user's situational needs. This work will investigate the theoretical foundations for user-controllable, interactive methods of recommending, will develop techniques that exploit social media data in conjunction with other sources, and will validate the research empirically in the area of e-commerce product recommendations. The methods developed are intended to be applicable in a wide range of recommending and decision support scenarios.
Starting Page	447
Ending Page	450
Page Count	4
File Format	PDF
ISBN	9781450340359
DOI	10.1145/2959100.2959104
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Interactive recommending User interfaces Trust Recommender systems
Content Type	Text
Resource Type	Article

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