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Found 11 papers, 1 papers with code
Rank-Preference Consistency as the Appropriate Metric for Recommender Systems
We propose what we consider to be a measure that is more fundamentally appropriate for assessing RS performance, rank-preference consistency, which simply counts the number of prediction pairs that are inconsistent with the user's expressed product preferences.
Is Arrow's Dictator a Drinker?
We critique the formulation of Arrow's no-dictator condition to show that it does not correspond to the accepted informal/intuitive interpretation.
Partial Proof of a Conjecture with Implications for Spectral Majorization
In this paper we report on new results relating to a conjecture regarding properties of $n\times n$, $n\leq 6$, positive definite matrices.
An Admissible Shift-Consistent Method for Recommender Systems
In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems.
Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems
In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e. g., AI-based.
Metric Search for Rank List Compatibility Matching with Applications
In this project, we proposed a new dating matching algorithm that uses Kendall-Tau distance to measure the similarity between users based on their ranking for items in a list.
A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System
In this paper we provide a latent-variable formulation and solution to the recommender system (RS) problem in terms of a fundamental property that any reasonable solution should be expected to satisfy.
Tensor Completion with Provable Consistency and Fairness Guarantees for Recommender Systems
We introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems.
On Use of the Moore-Penrose Pseudoinverse for Evaluating the RGA of Non-Square Systems
In this paper we note that the absence of the scale-invariance property by the conventional MP-RGA does not necessarily imply a practical disadvantage in real-world applications.
Covapixels
We propose and discuss the summarization of superpixel-type image tiles/patches using mean and covariance information.
A Canonical Image Set for Examining and Comparing Image Processing Algorithms
The purpose of this paper is to introduce a set of four test images containing features and structures that can facilitate effective examination and comparison of image processing algorithms.
