Working in Detail: How LSTM Hyperparameter Selection Influences Sentiment Analysis Results

Sentiment analysis of written customer reviews is a powerful way to generate knowledge about customer attitudes for future marketing activities. Meanwhile, Deep Learning as the most powerful machine learning method is of particular importance for sentiment analysis tasks. Due to this current relevance, an LSTM network based on a literature review to solve the challenging classification task of the IMDB LargeMovie Dataset is created. Hyperparameters are varied separately from each other to better understand their single influences on the overall model accuracy. Furthermore, we transformed variants with positive impacts into a final model in order to investigate whether the impacts can be cumulated. While preparing the amount of training data and the number of iteration steps resulted in a higher accuracy, pre-trained word vectors and higher network capacity did not work well separately. Even though implementing the variants with positive influences together raised the model´s performance, the improvement was lower than some single variants

Composition of Stochastic Transition Systems Based on Spans and Couplings

Conventional approaches for parallel composition of stochastic systems relate probability measures of the individual components in terms of product measures. Such approaches rely on the assumption that components interact stochastically independent, which might be too rigid for modeling real world systems. In this paper, we introduce a parallel-composition operator for stochastic transition systems that is based on couplings of probability measures and does not impose any stochastic assumptions. When composing systems within our framework, the intended dependencies between components can be determined by providing so-called spans and span couplings. We present a congruence result for our operator with respect to a standard notion of bisimilarity and develop a general theory for spans, exploiting deep results from descriptive set theory. As an application of our general approach, we propose a model for stochastic hybrid systems called stochastic hybrid motion automata

Investigating Machine Learning Techniques for Solving Product-line Optimization Problems

Product-line optimization using consumers’ preferences measured by conjoint analysis is an important issue to marketing researchers. Since it is a combinatorial NP-hard optimization problem, several meta-heuristics have been proposed to ensure at least near-optimal solutions. This work presents already used meta-heuristics in the context of product-line optimization like genetic algorithms, simulated annealing, particle-swarm optimization, and ant-colony optimization. Furthermore, other promising approaches like harmony search, multiverse optimizer and memetic algorithms are introduced to the topic. All of these algorithms are applied to a function for maximizing profits with a probabilistic choice rule. The performances of the meta-heuristics are measured in terms of best and average solution quality. To determine the most suitable metaheuristics for the underlying objective function, a Monte Carlo simulation for several different problem instances with simulated data is performed. Simulation results suggest the use of genetic algorithms, simulated annealing and memetic algorithms for product-line optimization

UGA or TAM: Which Approach Explains Digital Voice Assistant Acceptance Better?

Digital voice assistants (DVAs) have the potential to radically change the communication between companies and their customers in the near future. However, despite enormous cost and convenience reduction advantages for both sides, their acceptance is still limited and even tools for measuring their acceptance are missing. Consequently, in this paper, we investigate whether the Uses and Gratifications Approach (UGA) and/or the Technology Acceptance Model (TAM) is/are better suited for this purpose. We have a closer look on a popular DVA – Google Assistant – and investigate DVA acceptance in a navigation and sightseeing context using a field experiment and a follow-up questionnaire (n=173 participants). The results are promising: Both approaches (UGA and TAM) are valid tools. Pastime, expediency, and enjoyment demonstrate to be important drivers for using DVAs

Adaptive CBC : Are the Benefits Justifying its Additional Efforts Compared to CBC?

Currently, there is a big discussion ongoing among both practitioners and scientists whether the benefits of the Adaptive Choice-Based Conjoint (ACBC) analysis in comparison to (standard) Choice-Based Conjoint (CBC) analysis are justifying the additional costs and efforts of ACBC. To answer this question, recent studies in literature are reviewed and a conducted ACBC (n=205) about e-commerce in an international context is analyzed with regards to several aspects, e.g. excluded attribute levels and stimuli used for the Choice Tasks section. The results indicate that CBC is generally able to provide the main information about the most preferred attribute levels with less effort compared to ACBC. However, ACBC is very suitable for more complex products or services and for gaining deeper insights, such as information about the second-best options or completely unacceptable features. Furthermore, CBC requires a bigger sample size and is often less precise. Still, the related context will remain the main factor for or against the usage of one or the other method

Success Factors for Recommender Systems From a Customers’ Perspective

Recommender systems have become an integral part of today’s ecommerce landscape and are no longer only deployed on websites but also increasingly serve as a basis for the delivery of personalized product recommendations in various communication channels. Within this paper, we present a brief overview of popular and commonly used recommender algorithms as well as current cutting-edge algorithmic advances. We examine consumers’ preferences regarding product recommendations in advertisements across different media channels within the apparel industry by applying choice-based conjoint analysis. The findings of studies for young male (n = 170) and female (n = 162) consumers show that the recommender algorithm is not necessarily of upmost importance. In contrast, the advertising channel is of highest relevance with banner advertising being the least preferred channel. Moreover, differences between male and female respondents are outlined. Finally, implications for retailers and advertisers are discussed and a brief outlook on future developments is presented