Beyond Algorithms: An HCI Perspective on Recommender Systems

By Kirsten Swearingen, R. Sinha. 2001

349 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3882cfca774a26c3b0572fc5123f4eacc066aef8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Music recommendation and discovery in the long tail (2008, background): “Users like to receive some recommendations they already are familiar with (Swearingen and Sinha, 2001).”
Learning preferences of new users in recommender systems: an information theoretic approach (2008, background): “A number of surveys [32; 9] including the one we carried out in this experiment suggest that users of recommender systems generally understand that more ratings help the system learn their tastes better.”
Generating Predictive Movie Recommendations from Trust in Social Networks (2006, background): “Research has shown that people prefer recommendations from friends to those made by recommender systems [3] and that users prefer recommendations from systems they trust [4].”
E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact (2007, background): “Sinha and Swearingen, in a series of studies involving RAs in different domains (Sinha and Swearingen 2001, 2002; Swearingen and Sinha 2001, 166 MIS Quarterly Vol. 31 No. 1/March 2007 2002), also confirmed that explanations can enhance perceptions of the transparency of RAs’ inner workings, which…”
Collaborative Filtering Recommender Systems (2007, background): “Good examples include Swearingen and Sinha [59] and Dahlen et al.”
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, background): “In recent years, researchers have become more aware of the fact that effectiveness of recommender systems goes beyond recommendation accuracy (Swearingen and Sinha, 2001).”
Do social explanations work?: studying and modeling the effects of social explanations in recommender systems (2013, background): “part of the information presented about a recommendation, and studies show that explanations play an important role in helping a user evaluate a recommendation [23,26].”
Using Trust in Recommender Systems: An Experimental Analysis (2004, background): “User acceptance Human Computer Interaction studies [10] are needed to investigate the best ways to visualize the social network and how this could help the user in understanding the recommendation model and to control it.”
Recommender systems: from algorithms to user experience (2012, result): “But there was at first a powerful undercurrent, and then a growing consensus, that small changes in MAE were not the path to significant improvements in user experience (Swearingen and Sinha 2001).”
Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration (2018, methodology): “One of the first task-based studies was done by Swearingen and Sinha [40].”
FilmTrust: movie recommendations using trust in web-based social networks (2006, result): “Because the rating is personalized, originating from a social network, it is also in line with other results [3,4] that show users prefer recommendations from friends and trusted systems.”
Recommender Systems: Introduction and Challenges (2015, background): “Swearingen and Sinha [53] were among the first to point out that the effectiveness of an RS is dependent on factors that go beyond the quality of the prediction algorithm.”
Auralist: introducing serendipity into music recommendation (2012, methodology): “The use of accuracy as a performance metric is well-grounded; previous user studies, such as the one conducted by Swearingen and Sinha [23] indicate that recommendation accuracy is a primary factor behind user satisfaction with recommendation systems.”
Evaluating collaborative filtering recommender systems (2004, unknown): “Swearingen and Sinha [2001] found that users liked receiving some recommendations of items that they already were familiar with.”