UCERSTI 2: second workshop on user-centric evaluation of recommender systems and their interfaces

@inproceedings{Willemsen2010UCERSTI2S,
  title={UCERSTI 2: second workshop on user-centric evaluation of recommender systems and their interfaces},
  author={Martijn C. Willemsen and Dirk Bollen and Michael D. Ekstrand},
  booktitle={RecSys '11},
  year={2010}
}
Martijn Willemsen Human Technology Interaction group School of Innovation Sciences Eindhoven University of technology, The Netherlands m.c.willemsen@tue.nl Dirk Bollen Human Technology Interaction group School of Innovation Sciences Eindhoven University of technology, The Netherlands d.g.f.m.bollen@tue.nl Michael Ekstrand GroupLens Research Department of Computer Science and Engineering University of Minnesota, USA ekstrand@cs.umn.edu 
Conducting user experiments in recommender systems
TLDR
This tutorial teaches the essential skills involved in conducting user experiments, the scientific approach to user-centric evaluation, which are essential in uncovering how and why the user experience of recommender systems comes about.
Replicable Evaluation of Recommender Systems
TLDR
This tutorial shows how to present evaluation results in a clear and concise manner, while ensuring that the results are comparable, replicable and unbiased.
Modelling user preferences in multi-media recommender systems
TLDR
The final author version and the galley proof are versions of the publication after peer review and the final published version features the final layout of the paper including the volume, issue and page numbers.
Workshop on recommendation utility evaluation: beyond RMSE -- RUE 2012
TLDR
The RUE 2012 workshop sought to identify and better understand the current gaps in recommender system evaluation methodologies, help lay directions for progress in addressing them, and contribute to the consolidation and convergence of experimental methods and practice.
It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]
TLDR
It is proposed that recommender-system researchers should instead calculate metrics for time-series such as weeks or months, and plot the results in e.g. a line chart to show how algorithms' effectiveness develops over time, and hence the results allow drawing more meaningful conclusions about how an algorithm will perform in the future.
Evaluating the Accuracy and Utility of Recommender Systems
TLDR
It is concluded that current recommendation quality has outgrown the methods and metrics used for the evaluation of these systems, and qualitative approaches can be used, with minimal user interference, to correctly estimate the actual quality of recommendation systems.
Evaluating Recommender Systems: A Systemized Quantitative Survey
TLDR
The recommender evaluation guidelines (REval), which presents a roadmap for recommender systems' evaluators, is proposed, which provides stepwise guidelines for offline evaluation settings.
Recomendación de Contenidos Digitales basada en divergencias del lenguaje: Diseño, Experimentación y Evaluación
Para enfrentar el problema del descubrimiento de informacion en grandes repositorios de datos, como Internet, surgieron los sistemas de recomendacion. Estos sistemas ofrecen a los usuarios contenidos

References

SHOWING 1-7 OF 7 REFERENCES
Being accurate is not enough: how accuracy metrics have hurt recommender systems
TLDR
This paper proposes informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies, and proposes new user-centric directions for evaluating recommender systems.
Recsys'09 industrial keynote: top 10 lessons learned developing deploying and operating real-world recommender systems
TLDR
A number of lessons learned over the last ten years creating and operating recommender systems in a multitude of domains, from digital music to fitness plans through personal finance management, and inA multitude of business settings, from lightweight integrations to highly-coupled integrations within secure bank environments are shared.
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.
Interactive consumer decision aids
Today’s consumersare faced with avast and unprecedented breadth and depthof product alternatives: a Wal-Mart Supercenter stocks over 100,000 items(Yoffie 2005), Home Depot more than 50,000 (Murray
RecSys'10
  • RecSys'10
  • 2010
Top 10 lessons learned developing, deploying, and operating real-world recommender systems. http://recsys.acm.org/2009/invited_talk_strands_martin.pdf
  • 2009
Copyright is held by the author/owner(s)
  • Copyright is held by the author/owner(s)