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A new uncertainty-aware similarity for user-based collaborative filtering

Khadidja Belmessous, Faouzi Sebbak, M'hamed Mataoui, Walid Cherifi (2024)

Kybernetika

User-based Collaborative Filtering (UBCF) is a common approach in Recommender Systems (RS). Essentially, UBCF predicts unprovided entries for the target user by selecting similar neighbors. The effectiveness of UBCF greatly depends on the selected similarity measure and the subsequent choice of neighbors. This paper presents a new Uncertainty-Aware Similarity measure "UASim" which enhances CF by accurately calculating how similar, dissimilar, and uncertain users' preferences are. Uncertainty is...

A note on certain ergodicity coeflcients

Francesco Tudisco (2015)

Special Matrices

We investigate two ergodicity coefficients ɸ ∥∥ and τn−1, originally introduced to bound the subdominant eigenvalues of nonnegative matrices. The former has been generalized to complex matrices in recent years and several properties for such generalized version have been shown so far.We provide a further result concerning the limit of its powers. Then we propose a generalization of the second coefficient τ n−1 and we show that, under mild conditions, it can be used to recast the eigenvector problem...

Adaptive search heuristics for the generalized assignment problem.

Helena Ramalhinho Lourenço, Daniel Serra (2002)

Mathware and Soft Computing

The Generalized Assignment Problem consists of assigning a set of tasks to a set of agents at minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the agent's resource. We present the application of a MAX-MIN Ant System (MMAS) and a greedy randomized adaptive search procedure (GRASP) to the generalized assignment problem based on hybrid approaches. The MMAS heuristic can be seen as an adaptive sampling...

An analysis of selection sort using recurrence relations.

Francesc J. Ferri, Jesús Albert (1996)

Qüestiió

This paper presents a method for obtaining the expected number of data movements executed by the well-known Selection sort algorithm along with its corresponding variance. The approach presented here requires hardly any specific mathematical background. In particular, the average-case cost and variance are represented using recurrence relations whose solutions lead to the desired results. Even though this method is not applicable in general, it serves to conveniently present average-case algorithm...

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