• Corpus ID: 817299

The Decision-Theoretic Interactive Video Advisor

@article{Nguyen1999TheDI,
  title={The Decision-Theoretic Interactive Video Advisor},
  author={Hien Nguyen and Peter Haddawy},
  journal={ArXiv},
  year={1999},
  volume={abs/1301.6728}
}
The need to help people choose among large numbers of items and to filter through large amounts of information has led to a flood of research in construction of personal recommendation agents. One of the central issues in constructing such agents is the representation and elicitation of user preferences or interests. This topic has long been studied in Decision Theory, but surprisingly little work in the area of recommender systems has made use of formal decision-theoretic techniques. This… 

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References

SHOWING 1-10 OF 11 REFERENCES
Interactive Assessment of User Preference Models: The Automated Travel Assistant
TLDR
The Automated Travel Assistant is presented, an implemented prototype of the model that interactively builds flight itineraries using realtime airline information and has had over 4000 users between May and October 1996.
Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures
TLDR
This paper proposes eliciting the preferences of a new user interactively and incrementally, using the closest existing preference structures as potential defaults, and takes the first step of studying various distance measures over fully and partially specified preference structures.
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.
Faster random generation of linear extensions
Decisions with Multiple Objectives: Preferences and Value Trade-Offs
TLDR
This book describes how a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives.
Recommender Systems: A GroupLens Perspective
TLDR
Apparatus for applying labels onto hollow bodies, including a blow mold having a cavity for containing a hollow body, and a member movable toward and away from the mold cavity, with suction openings provided for securely maintaining the label in place on the member.
Recommendation as Classification: Using Social and Content-Based Information in Recommendation
TLDR
These sulfoxides and sulfones are useful as analgesics, non-addicting narcotic antagonists and anti-diarrheal agents and having agonist activity at opiate receptors are disclosed herein.
Counting linear extensions is #P-complete
We show that the problem of counting the number of linear extensions of a given partially ordered set is #P-complete. This settles a long-standing open question and contrssts with recent results
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.
Decisions with Multiple Objectives—Preferences and Value Tradeoffs
...
...