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} }
51 Citations
Probabilistic Evaluation of Preference Aggregation Functions: A Statistical Approach in Social Choice Theory
- Economics, Computer Science
- 2016
A statistical criterion for evaluating the appropriateness of preference aggregation functions for a fixed group of persons by relying on probabilistic information on the homogeneity structure of the group members’ preferences is introduced.
Predicting User Preferences Via Similarity-Based Clustering
- Computer ScienceCanadian Conference on AI
- 2008
This paper explores the idea of clustering partial preference relations as a means for agent prediction of users' preferences by exploiting the notion that people with common preferences over a given set of outcomes will likely have common interests over other outcomes.
A study of similarity measures through the paradigm of measurement theory: the classic case
- Computer ScienceSoft Comput.
- 2019
The paper aims at providing information on similarity measures that can help in choosing “a priori” one of them on the basis of the semantics behind this choice, from the point of view of the ranking relation they induce on object pairs.
A study of similarity measures through the paradigm of measurement theory: the classic case
- Computer ScienceSoft Computing
- 2019
The paper aims at providing information on similarity measures that can help in choosing “a priori” one of them on the basis of the semantics behind this choice, from the point of view of the ranking relation they induce on object pairs.
du CNRC ( NPArC ) Predicting User Preferences via Similarity-Based Clustering
- Computer Science
- 2010
This paper explores the idea of clustering partial preference relations as a means for agent prediction of users’ preferences by clustering similar users together, exploiting the notion that people with common preferences over a given set of outcomes will likely have common interests over other outcomes.
Learning User Preference Models under Uncertainty for Personalized Recommendation
- Computer Science
- 2006
A novel knowledge representation method for item and user preference is proposed that accounts for uncertainty due to the subjectivity, vagueness and imprecision using concepts from the fuzzy set and logic theory and outperformed the state of the art approaches in terms of precision, recall, and F1-measure.
Pairwise Preference Learning and Ranking
- Computer ScienceECML
- 2003
The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example.
Knowledge-based acquisition of tradeoff preferences for negotiating agents
- Computer ScienceICEC '03
- 2003
It is identified that user trade-off preferences play a fundamental role in negotiation in general and another method is developed to remove the limitation of the high user workload of the exhaustive method.
Acquiring user tradeoff strategies and preferences for negotiating agents: A default-then-adjust method
- Computer Science, EconomicsInt. J. Hum. Comput. Stud.
- 2006
References
SHOWING 1-10 OF 41 REFERENCES
Preference Elicitation via Theory Refinement
- Computer ScienceJ. Mach. Learn. Res.
- 2003
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
- Computer ScienceUAI
- 1999
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
- Economics, Computer ScienceUAI
- 1998
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
- Computer ScienceUAI
- 1998
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
- Computer ScienceUAI
- 1999
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
- Economics
- 1979
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…
Parametric models of the utility of survival duration: Tests of axioms in a generic utility framework
- Economics
- 1989
Theory of Games and Economic Behavior
- EconomicsNature
- 1946
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…
Metric Methods for Analyzing Partially Ranked Data
- Mathematics, Computer Science
- 1986
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.