Similarity of personal preferences: Theoretical foundations and empirical analysis

@article{Ha2003SimilarityOP,
  title={Similarity of personal preferences: Theoretical foundations and empirical analysis},
  author={Vu A. Ha and Peter Haddawy},
  journal={Artif. Intell.},
  year={2003},
  volume={146},
  pages={149-173}
}

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References

SHOWING 1-10 OF 41 REFERENCES

Preference Elicitation via Theory Refinement

We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We demonstrate that when domain knowledge is

The Decision-Theoretic Interactive Video Advisor

DIVA is described, a decision-theoretic agent for recommending movies that contains a number of novel features and has a rich representation of preference, distinguishing between a user's general taste in movies and his immediate interests.

Utility Elicitation as a Classification Problem

This work attempts to identify the new user's utility function based on classification relative to a database of previously collected utility functions by identifying clusters of utility functions that minimize an appropriate distance measure.

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.

A Hybrid Approach to Reasoning with Partially Elicited Preference Models

This work shows how comparative statements about classes of decision alternatives can be used to further constrain the utility function and thus identify supoptimal alternatives, and demonstrates that quantitative and qualitative approaches can be synergistically integrated to provide effective and flexible decision support.

Prospect theory: analysis of decision under risk

Analysis of decision making under risk has been dominated by expected utility theory, which generally accounts for people's actions. Presents a critique of expected utility theory as a descriptive

Theory of Games and Economic Behavior

THIS book is based on the theory that the economic man attempts to maximize his share of the world's goods and services in the same way that a participant in a game involving many players attempts to

Faster random generation of linear extensions

Metric Methods for Analyzing Partially Ranked Data

This chapter discusses metrics on Fully Ranked Data, the Tied Ranks approach to Metrizing Partially Ranked data, and the Hausdorff Distances between Different Types of Partially ranked data.