Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN

Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleting perturbation is a common evaluation method. This word removal approach, however, disregards any linguistic dependencies that may exist between words or phrases in a sentence, which could semantically guide a classifier to a particular prediction. In this paper, we present a feature-based evaluation framework for comparing the two attribution methods on customer reviews (public data sets) and Customer Due Diligence (CDD) extracted reports (corporate data set). Instead of removing words based on the relevance score, we investigate perturbations based on embedded features removal from intermediate layers of Convolutional Neural Networks. Our experimental study is carried out on embedded-word, embedded-document, and embedded-ngrams explanations. Using the proposed framework, we provide a visualization tool to assist analysts in reasoning toward the model's final prediction.Comment: NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, Montr\'eal, Canad

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019

Automatic Environmental Sound Recognition: Performance versus Computational Cost

In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition (AESR) algorithms are most often developed with limited consideration for computational cost, this article seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance em as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost

From physical marketing to web marketing

Reviews the criticism of the 4P marketing mix framework as the basis of traditional and virtual marketing planning. Argues that the customary marketing management approach, based on the popular marketing mix 4Ps paradigm, is inadequate in the case of virtual marketing. Identifies two main limitations of the marketing mix when applied in online environments namely the role of the Ps in a virtual commercial setting and the lack of any strategic elements in the model. Identifies the critical factors of the Web marketing and argues that the basis for successful e-commerce is the full integration of virtual activities into the company's physical strategy, marketing plan and organisational processes. The 4S elements of the Web marketing mix framework offer the basis for developing and commercialising business to consumer online projects. The model was originally developed for educational purposes and has been tested and refined by means of three case studies

Buying High Tech Products

Prior research on technology-intensive (TI) markets makes abstraction of the social context in which transactions take place. In contrast with this prior literature, the authors show that buyer-vendor transactions in TI markets are relationally and structurally embedded in an interfirm network. Their main premise is that buyers in TI markets prefer vendors with whom they can share a strong tie, and that in turn buyers want these vendors to share strong ties with their component manufacturers. This is an important addition to TI literature and to the on-going debate on the strength of ties in the sociology, management and marketing literatures. The authors also specifically consider how characteristics focal to TI markets, such as the know-how buyers possess or the pace of technological change they perceive, affect the extent to which buying behavior is relationally and structurally embedded. An empirical test in the computer network market shows good support for the developed theory.tie strength;embeddedness;buying behavior;conjoint analysis;technology-intensive markets

A look at cloud architecture interoperability through standards

Enabling cloud infrastructures to evolve into a transparent platform while preserving integrity raises interoperability issues. How components are connected needs to be addressed. Interoperability requires standard data models and communication encoding technologies compatible with the existing Internet infrastructure. To reduce vendor lock-in situations, cloud computing must implement universal strategies regarding standards, interoperability and portability. Open standards are of critical importance and need to be embedded into interoperability solutions. Interoperability is determined at the data level as well as the service level. Corresponding modelling standards and integration solutions shall be analysed

Evaluating the quality of inter-organisational relationships: does one plus one equal only two?

Inter-organisational relations have increasingly become an 18obligation 19 for individual organisations in all sectors of the political, social and economic spheres. 18The major factors that organisations must take into account are other organisations 19, Aldrich argues (1994; cited by Sydow, 2002: 141). Therefore, organisational relationships emerge as a consequence of purposeful interactions constrained and enabled by different organisational structures and values (Sydow, 2002). The quality of these relationships are identified and examined in this paper. This is presented through the definition and characteristics of inter-organisational relationships, definition and attributes of the romantic approach to quality and the proposition of qualitative evaluation as a possible approach for assessing the quality of such relationships. Secondary data however shows that the applicability of such a research is not always valuable