Knowing the unknown: visualising consumption blind-spots in recommender systems

@article{Tintarev2018KnowingTU,
  title={Knowing the unknown: visualising consumption blind-spots in recommender systems},
  author={Nava Tintarev and Shahin Rostami and Barry Smyth},
  journal={Proceedings of the 33rd Annual ACM Symposium on Applied Computing},
  year={2018}
}
In this paper we consider how to help users to better understand their consumption profiles by examining two approaches to visualising user profiles - chord diagrams, and bar charts - aimed at revealing to users those regions of the recommendation space that are unknown to them, i.e. blind-spots. Both visualisations do this by connecting profile preferences with a filtered recommendation space. We compare and contrast the two visualisations in a live user study (n = 70). The results suggest… 

Figures and Tables from this paper

Using Visualizations to Encourage Blind-Spot Exploration
TLDR
The results confirmed that users with higher understanding of their profile tend to explore their blind-spot categories more, and the effectiveness of two visualizations for increasing a user’s intention to explore new content was compared.
TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems
TLDR
TastePaths is an interactive web tool that helps users explore an overview of the genre-space via a graph of connected artists and discusses opportunities and challenges for incorporating more control and expressive feedback in recommendation systems to help users explore spaces beyond their immediate interests and improve these systems’ underlying algorithms.
Effects of Individual Traits on Diversity-Aware Music Recommender User Interfaces
TLDR
The research findings show the necessity of considering individual traits while designing diversity-aware interfaces and how individual traits such as musical sophistication (MS) and visual memory (VM) influence the satisfaction of the visualization for perceived music diversity, overall usability, and support to identify blind-spots.
NewsViz: Depicting and Controlling Preference Profiles Using Interactive Treemaps in News Recommender Systems
TLDR
NewsViz is introduced, a RS that visualizes the domain space of online news as treemap, which can interactively be manipulated to personalize a feed of suggested news articles and found that the degree of overview of the item domain influenced the perceived quality of recommendations.
Recommender system for developing new preferences and goals
TLDR
The research will take a multidisciplinary approach in which insights from psychology on decision making and habit formation are paired with new approaches to recommendation that included preference evolution, interactive exploration methods and goal-directed approaches to evaluate the success of such algorithms.
Same, Same, but Different: Algorithmic Diversification of Viewpoints in News
TLDR
This paper introduces an approach to automatically identifying content that represents a wider range of opinions on a given topic and confirms that user acceptance of this diversification also needs to be addressed in tandem to enable a complete solution.
Reading News with a Purpose: Explaining User Profiles for Self-Actualization
TLDR
This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations, and finds that providing users with different goals for self-actualization influences their reading intentions for news recommendations.
PaRIS: Polarization-aware Recommender Interactive System
TLDR
This paper proposes a new counter-polarization approach for existing Matrix Factorization based recommender systems, that can be tuned by a user-controlled counter-Polarization parameter which serves like a voluntary user anti- polarization or discovery dial.
Personalized Recommendations for Music Genre Exploration
TLDR
This is one of the first studies using a recommender system to support users' preference development, and provides insights in how recommender systems can help users attain new goals and tastes.
Understanding Echo Chambers in E-commerce Recommender Systems
TLDR
The echo chamber phenomenon in Alibaba Taobao --- one of the largest e-commerce platforms in the world --- is analyzed and evidence suggests the tendency of echo chamber in user click behaviors, while it is relatively mitigated in user purchase behaviors.
...
...

References

SHOWING 1-10 OF 11 REFERENCES
Exploring the filter bubble: the effect of using recommender systems on content diversity
TLDR
This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users and contributes a novel metric to measure content diversity based on information encoded in user-generated tags, and presents a new set of methods to examine the temporal effect of recommender systems on the user experience.
What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs
TLDR
Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency.
Understanding and controlling the filter bubble through interactive visualization: a user study
TLDR
A design of an interactive visualization is proposed, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism.
Explaining collaborative filtering recommendations
TLDR
This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.
Inspection Mechanisms for Community-based Content Discovery in Microblogs
TLDR
A formative evaluation of an interface for inspecting microblog content aims to improving content discovery, while maintaining content relevance and sense of user control, and proposes improvements and a mock-up for an interface to be used for future larger scale experiments.
Crowd-Based Personalized Natural Language Explanations for Recommendations
TLDR
A scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes are described.
Exposure to ideologically diverse news and opinion on Facebook
TLDR
Examination of the news that millions of Facebook users' peers shared, what information these users were presented with, and what they ultimately consumed found that friends shared substantially less cross-cutting news from sources aligned with an opposing ideology.
The MovieLens Datasets: History and Context
TLDR
The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented.
Filter Bubbles, Echo Chambers, and Online News Consumption
Online publishing, social networks, and web search have dramatically lowered the costs of producing, distributing, and discovering news articles. Some scholars argue that such technological changes
Partisan asymmetries in online political activity
TLDR
In contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.
...
...