Show Me the Money: Dynamic Recommendations for Revenue Maximization

Recommender Systems (RS) play a vital role in applications such as e-commerce and on-demand content streaming. Research on RS has mainly focused on the customer perspective, i.e., accurate prediction of user preferences and maximization of user utilities. As a result, most existing techniques are not explicitly built for revenue maximization, the primary business goal of enterprises. In this work, we explore and exploit a novel connection between RS and the profitability of a business. As recommendations can be seen as an information channel between a business and its customers, it is interesting and important to investigate how to make strategic dynamic recommendations leading to maximum possible revenue. To this end, we propose a novel \model that takes into account a variety of factors including prices, valuations, saturation effects, and competition amongst products. Under this model, we study the problem of finding revenue-maximizing recommendation strategies over a finite time horizon. We show that this problem is NP-hard, but approximation guarantees can be obtained for a slightly relaxed version, by establishing an elegant connection to matroid theory. Given the prohibitively high complexity of the approximation algorithm, we also design intelligent heuristics for the original problem. Finally, we conduct extensive experiments on two real and synthetic datasets and demonstrate the efficiency, scalability, and effectiveness our algorithms, and that they significantly outperform several intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity proo

Faire de l’histoire au théâtre au xvie siècle : les triolets dans La Vie monseigneur sainct Loÿs par personnaiges de Pierre Gringore*

Dépeint comme un poète romantique par Victor Hugo et Théodore de Banville et classé parmi les « grands rhétoriqueurs » depuis Anatole de Montaiglon, Pierre Gringore est avant tout un auteur polyvalent, actif sous les règnes de Louis XII et François Ier. Dramaturge productif, mais aussi juriste de formation et éditeur de ses propres œuvres en tant que libraire, il incarne l’écrivain moraliste et polémiste, maître de la parole publique. La Vie monseigneur sainct Loÿs par personnaiges occupe une..

Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification

Linear mixed-effects model for longitudinal complex data with diversified characteristics

International audienc

Deviations from Beer's law in electronic absorption and circular dichroism: Detection for enantiomeric excess analysis

The electronic absorption (UV) to circular dichroism (CD) signal ratio can be used for enantiomeric excess (ee) analysis within linear range. However, CD detection often requires a high sample concentration where deviations from Beer's law may occur. Individual enantiomers of four chiral compounds were separated from commercial racemates by semipreparative high‐performance liquid chromatography (HPLC) with chiral columns. They were used to trace possible deviations in both UV and CD detection on achiral HPLC with a photodiode array detector and a CD detector. The CD/UV ratios for samples with the same ee value decreased by up to 7.8 to 52% when the injection volume increased, indicating that the linear standard curve of ee versus CD/UV is only valid within a narrow range. To extend the sample amount to a wider range, a data‐processing method was developed based on two second‐order polynomial functions, which were constructed to fit the relationship between the intensities of the UV and CD signals for two enantiomers. Moreover, a more simplified method based on a third‐order polynomial function was established to calculate the ee values. The variations between the predicted and experimental ee values were within ±0.08 for both methods. To our knowledge, this is the first study that the deviations from Beer's law are considered in both UV and CD detection for ee analysis

Dismantling the Black Box: Understanding Consumers\u27 Motivations for the Usage of Live Streaming Shopping Platform

Capturing consumers’ motivations for using the live streaming shopping platform (LSSP) can help guide the optimization of the platform and enhance shopping experience of consumers while watching live videos. Previous studies on user motivation typically explored technical and psychological antecedents of usage by considering the platform holistically. However, this black-box like treatment to the platform blurs the finer-grained details of consumer usage. This study takes a micro-level approach, disassembling the LSSP into 13 representative design features, and refines nine user motivations based on the uses and gratifications theory. Through collecting 237 questionnaires and employing regression analysis, we reveal the nuanced relationship between platform design features and consumer motivations. Our findings show that different design features are driven by distinct motivations, diverging from overall LSSP usage motivations. This research broadens the scope of LSSP studies, improves platform functionality, and offers practical insights for service providers