A Visual Interface for Social Information Filtering

O’Donovan, John; Gretarsson, Brynjar; Bostandjiev, Svetlin; Höllerer, Tobias; Smyth, Barry

doi:10.1109/cse.2009.26

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…The explanation interface was associated with the perception of recommendation quality [37], gaining trust in the system [7] and experiencing the competence of the system [45]. The studies of [13,27,26] have all mentioned that providing a controllable interface in the social recommender system can increase overall user satisfaction. The authors adopted an interactive graphical interface to present a social recommender that enables control on an item or user-level preference in a collaborative recommender system.…”

Section: Introductionmentioning

confidence: 99%

Beyond the Ranked List

Tsai

Brusilovsky

2018

23rd International Conference on Intelligent User Interfaces

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The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this paper, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users' subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs.

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Section: Introductionmentioning

confidence: 99%

Beyond the Ranked List

Tsai

Brusilovsky

2018

23rd International Conference on Intelligent User Interfaces

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“…More recent work [16,35,37] discusses interactive graphical representations of the recommendation process, to enable control over both item-and user-level preferences in collaborative recommender systems.…”

Section: Controlmentioning

confidence: 99%

“…Social recommenders limit the set of other users to your friends, thereby leveraging personal connections [35,37,50]. We suspect that users of social recommenders may not be satisfied with only a static list of recommendations.…”

Section: Introductionmentioning

confidence: 99%

Inspectability and control in social recommenders

Knijnenburg

Bostandjiev

O’Donovan

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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102

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Users of social recommender systems may want to inspect and control how their social relationships influence the recommendations they receive, especially since recommendations of social recommenders are based on friends rather than anonymous "nearest neighbors". We performed an online user experiment (N=267) with a Facebook music recommender system that gives users control over the recommendations, and explains how they came about. The results show that inspectability and control indeed increase users' perceived understanding of and control over the system, their rating of the recommendation quality, and their satisfaction with the system.

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“…In the case of control and transparency, these traits are often sought by way of visual and interactive mechanisms, e.g. to elicit user preference and explain the recommendation process [11], tweak and assess the influence of neighbors in collaborative filtering [46], depict relationships among multiple agents in social RS [74] or control hybrid recommendations [51].…”

Section: Control and Transparencymentioning

confidence: 99%

Understanding the effects of control and transparency in searching as learning

Sciascio

Veas

Barria-Pineda

et al. 2020

Proceedings of the 25th International Conference on Intelligent User Interfaces

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In this paper, we analyze the benefits of adopting user interfaces that offer control and transparency for searching in contexts of learning activities. Concretely, we conducted a user study with pharmacy students performing a problem-solving task in the course of a university lecture. The task involved finding scientific papers containing relevant information to solve a clinical case. Students were split into two independent groups and assigned one search tool to perform the task. The baseline group worked with PubMed, a popular search engine in the life sciences domain, whereas the second half of the class was assigned an exploratory search system (ESS) designed for control and transparency. In the analysis, we cover the objective and subjective dimensions of the task outcomes. Firstly, the objective analysis addresses the inherent difficulty of the search task in a learning scenario and identifies certain improvements in performance for those students using the ESS, most notably when searching for primary-source content. The subjective analysis investigates the human factors side, providing evidence that the ESS effectively increases the perception of control and transparency and is able to produce a better user experience. Lastly, we report on perceived learning as a subjective dimension measured separately from user experience. CCS CONCEPTS • Information systems → Web searching and information discovery; Search interfaces; • Applied computing → Interactive learning environments.

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A Visual Interface for Social Information Filtering

Cited by 14 publications

References 12 publications

Beyond the Ranked List

Beyond the Ranked List

Inspectability and control in social recommenders

Understanding the effects of control and transparency in searching as learning

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