Exploring the filter bubble: the effect of using recommender systems on content diversity

@article{Nguyen2014ExploringTF,
  title={Exploring the filter bubble: the effect of using recommender systems on content diversity},
  author={Tien T. Nguyen and Pik-Mai Hui and F. Maxwell Harper and Loren G. Terveen and Joseph A. Konstan},
  journal={Proceedings of the 23rd international conference on World wide web},
  year={2014}
}
Eli Pariser coined the term 'filter bubble' to describe the potential for online personalization to effectively isolate people from a diversity of viewpoints or content. [] Key Method We contribute a novel metric to measure content diversity based on information encoded in user-generated tags, and we present a new set of methods to examine the temporal effect of recommender systems on the user experience. We do find that recommender systems expose users to a slightly narrowing set of items over time. However…
Recommenders with a Mission: Assessing Diversity in News Recommendations
TLDR
This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system, and proposes a set of metrics grounded in social science interpretations of diversity.
SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments
TLDR
It is argued that simulating the effects of recommender systems can help content providers to make more informed decisions when choosing algorithmic recommenders, and as such can help mitigate the aforementioned societal concerns.
Presenting Diversity Aware Recommendations: Making Challenging News Acceptable
TLDR
This paper presents a vision of a diversity aware recommendation model, for the selection and presentation of a diverse selection of news to users, and aims to maximize the amount of diverse content that users are exposed to, without damaging system reputation.
Reducing the filter bubble effect on Twitter by considering communities for recommendations
TLDR
A thorough study of communities on a large Twitter data set that quantifies the effect of recommender systems on users’ behavior by creating filter bubbles is presented and the CAM approach, which relies on similarities between communities to re-rank lists of recommendations to weaken the filter bubble effect for these users is proposed.
Community-Based Recommendations on Twitter: Avoiding the Filter Bubble
TLDR
The Community Aware Model (CAM) is proposed to counter the impact of different recommender systems on information consumption and show that filter bubbles concern up to 10% of users and the model based on similarities between communities enhance recommender Systems.
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.
Injecting Semantic Diversity in Top-N Recommender Systems Using Determinantal Point Processes and Curated Lists
TLDR
This paper proposes an approach to improve the diversity of results generated by Top-N recommender systems, by using Determinantal Point Processes (DPPs) over user curated lists in the movie domain and incorporating them to rerank the Top- NRecommender systems.
An Empirical Analysis on Transparent Algorithmic Exploration in Recommender Systems
TLDR
A recommender interface that reveals which items are for exploration and conducted a within-subject study with 94 MTurk workers indicated that users left significantly more feedback on items chosen for exploration with the interface, and path analysis show that, in only the new interface, exploration caused to increase user-centric evaluation metrics.
Preference Amplification in Recommender Systems
TLDR
This work proposes a theoretical framework for studying preference amplification in a matrix factorization based recommender system, and model the dynamics of the system, where users interact with the recommender systems and gradually "drift'' toward the recommended content, with theRecommender system adapting, based on user feedback, to the updated preferences.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 30 REFERENCES
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
TLDR
This paper examines the effect of recommender systems on the diversity of sales, and shows how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers' preferences.
Will the Global Village Fracture into Tribes : Recommender Systems and their Effects on Consumers
Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer’s preferences and recommend content best suited to him (e.g., “Customers who
Improving recommendation lists through topic diversification
TLDR
This work presents topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests, and introduces the intra-list similarity metric to assess the topical diversity of recommendation lists.
Item-based collaborative filtering recommendation algorithms
TLDR
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Rating support interfaces to improve user experience and recommender accuracy
TLDR
This study introduces interfaces that provide methods for supporting the mapping process for recommender systems by reminding the user of characteristics of items by providing personalized tags and relating rating decisions to prior rating decisions using exemplars.
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
TLDR
This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.
GroupLens: an open architecture for collaborative filtering of netnews
TLDR
GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact
TLDR
A conceptual model with 28 propositions derived from five theoretical perspectives is developed that identifies other important aspects of RAs, namely RA use, RA characteristics, provider credi'r, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RA.
The Filter Bubble: What the Internet Is Hiding from You
Author Q&A with Eli Pariser Q: What is a Filter Bubble? A: Were used to thinking of the Internet like an enormous library, with services like Google providing a universal map. But thats no longer
...
1
2
3
...