Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders

The evaluation of recommendation systems is a complex task. The offline and online evaluation metrics for recommender systems are ambiguous in their true objectives. The majority of recently published papers benchmark their methods using ill-posed offline evaluation methodology that often fails to predict true online performance. Because of this, the impact that academic research has on the industry is reduced. The aim of our research is to investigate and compare the online performance of offline evaluation metrics. We show that penalizing popular items and considering the time of transactions during the evaluation significantly improves our ability to choose the best recommendation model for a live recommender system. Our results, averaged over five large-size real-world live data procured from recommenders, aim to help the academic community to understand better offline evaluation and optimization criteria that are more relevant for real applications of recommender systems.Comment: Accepted to evalRS 2023@KD

‘Only Slightly Does the Water Separate Us!’. On Flowing, the Vanishing Point, and Navigation

The topic of our deliberation is resonance as an issue of understanding ourselves and the world, — an understanding that is undoubtedly related to speech. Resonance, as an issue, opened by senses in the world; an issue opening for senses and in the world; resonance as a matter of senses, whose sensorium commune is the body: becoming of the body (the body-becoming), therefore always a question of identity and difference (identifying and identified, marking and marked, differentiating and differentiated). The subject of our deliberation is the undulation that shapes cannot be represented by the shape of the wave or by the sum of individual shapes of the waves (any confirmation, reassuring of My-self in a shape or by a shape is always seriously threatened by the disintegration of Me). So, if Eastern thought says “the shape is empty”, besides the philosopher of being and existence, besides the phenomenologist, besides the metaphysician, besides the philosopher of the body, besides the philosopher of significance, in our reflection we also recognize the philosopher of emptiness.678

Building, implementation, promotion and sale of jewelry and glass at the international level

52 s. :il., grafy +CD ROMPráce se zabývá propojením špičkového výrobního know-how a zavádění výroby mimo Evropskou unii. Pojednává o silných a slabých stránkách tohoto projektu. Čtenář se dozví podstatné informace o mezinárodním obchodu, marketingu i samotné výrobě na indické půdě. Tato práce rovněž obsahuje část praktickou, kde čtenář pozná detailní proces zavádění, budou popsány jednotlivé a konkrétní kroky projektu. Čtenář se zde seznámí s vývojem projektu až do současnosti, stejně jako dojde k představení obou stran podílejících se na projektu. V závěru čtenář pak najde návrh řešení pro budoucnost

A meta-learning based framework for building algorithm recommenders: An application for educational arena

The task of selecting the most suitable classification algorithm for each data set under analysis is still today a unsolved research problem. This paper therefore proposes a meta-learning based framework that helps both, practitioners and non-experts data mining users to make informed decisions about the goodness and suitability of each available technique for their data set at hand. In short, the framework is supported by an experimental database that is fed with the meta-features extracted from training data sets and the performance obtained by a set of classifiers applied over them, with the aim of building an algorithm recommender using regressors. This will allow the end-user to know, for a new unseen data set, the predicted accuracy of this set of algorithms ranked by this value. The experimentation performed and discussed in this paper is addressed to evaluate which meta-features are more significant and useful for characterising data sets with the end goal of building algorithm recommenders and to test the feasibility of these recommenders. The study is carried out on data sets from the educational arena, in particular, targeted to predict students' performance in e-learning courses

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results

A large number of classification algorithms have been proposed in the machine learning literature. These algorithms have different pros and cons, and no algorithm is the best for all datasets. Hence, a challenging problem consists of choosing the best classification algorithm with its best hyper-parameter settings for a given input dataset. In the last few years, Automated Machine Learning (Auto-ML) has emerged as a promising approach for tackling this problem, by doing a heuristic search in a large space of candidate classification algorithms and their hyper-parameter settings. In this work we propose an improved version of our previous Evolutionary Algorithm (EA) – more precisely, an Estimation of Distribution Algorithm – for the Auto-ML task of automatically selecting the best classifier ensemble and its best hyper-parameter settings for an input dataset. The new version of this EA was compared against its previous version, as well as against a random forest algorithm (a strong ensemble algorithm) and a version of the well-known Auto-ML method Auto-WEKA adapted to search in the same space of classifier ensembles as the proposed EA. In general, in experiments with 21 datasets, the new EA version obtained the best results among all methods in terms of four popular predictive accuracy measures: error rate, precision, recall and F-measure