Explaining collaborative filtering recommendations

@inproceedings{Herlocker2000ExplainingCF,
  title={Explaining collaborative filtering recommendations},
  author={Jonathan L. Herlocker and Joseph A. Konstan and John Riedl},
  booktitle={CSCW '00},
  year={2000}
}
Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address… Expand
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References

SHOWING 1-10 OF 17 REFERENCES
GroupLens: applying collaborative filtering to Usenet news
TLDR
The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high. Expand
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. Expand
Social information filtering: algorithms for automating “word of mouth”
TLDR
The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared. Expand
Questions and information systems
TLDR
This chapter discusses the evolution of questions in Successive Versions of an Expert System for Real Estate Disposition and the role of Question Asking in Learning From Computer-Based Decision Aids. Expand
Recommending and evaluating choices in a virtual community of use
TLDR
A general history-of-use method that automates a social method for informing choice and report on how it fares in the context of a fielded test case: the selection of videos from a large set of videos. Expand
An explanatory and “argumentative” interface for a model-based diagnostic system
TLDR
It is concluded that sufficient information exists in a model-based system to provide a wide range of explanation types, and that, the discourse approach is a convenient, powerful and broadly applicable method of organizing and controlling information exchange involving this data. Expand
Explanation facilities and interactive systems
TLDR
Research is reported which identifies both the strengths and weaknesses of current research and shows how to overcome those weaknesses. Expand
Decision theory in expert systems and artificial intelligenc
TLDR
The potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision-theoretic framework is surveyed and the belief network and influence diagram representations are presented. Expand
An algorithmic framework for performing collaborative filtering
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercialExpand
The MYCIN Experiments of the Stanford Heuristic Programming Project
Main entry under title: Rule-based expert systems.
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