Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution

Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowledge graphs available, we can use this additional information to predict the time series better. Recently, there has been a focus on the application of deep representation learning on dynamic graphs. These methods predict the structure of the graph by reasoning over the interactions in the graph at previous time steps. In this paper, we propose a new framework to incorporate the information from dynamic knowledge graphs for time series prediction. We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy. Our framework, DArtNet, learns a static embedding for every node in the graph as well as a dynamic embedding which is dependent on the dynamic attribute value (time-series). Then it captures the information from the neighborhood by taking a relation specific mean and encodes the history information using RNN. We jointly train the model link prediction and attribute prediction. We evaluate our method on five specially curated datasets for this problem and show a consistent improvement in time series prediction results. We release the data and code of model DArtNet for future research at https://github.com/INK-USC/DArtNet .Comment: In Proceedings of IJCAI 2020. Code can be found at https://github.com/INK-USC/DArtNet . The sole copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. Original Publication available at https://www.ijcai.org/Proceedings/2020/38

Adoption of Artificial Intelligence in an Organizational Context: Analysis of the Factors Influencing the Adoption and Decision-Making Process

The emergence of Artificial Intelligence (AI) shifts the business environment to such an extent that this general-purpose technology (GPT) is prevalent in a wide range of industries, evolves through constant advancements, and stimulates complementary innovations. By implementing AI applications in their business practices, organizations primarily benefit from improved business process automation, valuable cognitive insights, and enhanced cognitive engagements. Despite this great potential, organizations encounter difficulties in adopting AI as they struggle to adjust to corresponding complex organizational changes. The tendency for organizations to face challenges when implementing AI applications indicates that AI adoption is far from trivial. The complex organizational change generated by AI adoption could emerge from intelligent agents’ learning and autonomy capabilities. While AI simulates human intelligence in perception, reasoning, learning, and interaction, organizations’ decision-making processes might change as human decision-making power shifts to AI. Furthermore, viewing AI adoption as a multi-stage rather than a single-stage process divides this complex change into the initiation, adoption, and routinization stages. Thus, AI adoption does not necessarily imply that AI applications are fully incorporated into enterprise-wide business practices; they could be at certain adoption stages or only in individual business functions. To address these complex organizational changes, this thesis seeks to examine the dynamics surrounding AI adoption at the organizational level. Based on four empirical research papers, this thesis presents the factors that influence AI adoption and reveals the impact of AI on the decision-making process. These research papers have been published in peer-reviewed conference proceedings. The first part of this thesis describes the factors that influence AI adoption in organizations. Based on the technology-organization-environment (TOE) framework, the findings of the qualitative study are consistent with previous innovation studies showing that generic factors, such as compatibility, top management, and data protection, affect AI adoption. In addition to the generic factors, the study also reveals that specific factors, such as data quality, ethical guidelines, and collaborative work, are of particular importance in the AI context. However, given these technological, organizational, and environmental factors, national cultural differences may occur as described by Hofstede’s national cultural framework. Factors are validated using a quantitative research design throughout the adoption process to account for the complexity of AI adoption. By considering the initiation, adoption, and routinization stages, differentiating and opposing effects on AI adoption are identified. The second part of this thesis addresses AI’s impact on the decision-making process in recruiting and marketing and sales. The experimental study shows that AI can ensure procedural justice in the candidate selection process. The findings indicate that the rule of consistency increases when recruiters are assisted by a CV recommender system. In marketing and sales, AI can support the decision-making process to identify promising prospects. By developing classification models in lead-and-opportunity management, the predictive performances of various machine learning algorithms are presented. This thesis outlines a variety of factors that involve generic and AI-specific considerations, national cultural perspectives, and a multi-stage process view to account for the complex organizational changes AI adoption entails. By focusing on recruiting as well as marketing and sales, it emphasizes AI’s impact on organizations’ decision-making processes

Relational Time Series Forecasting for Retail Drugstores: A Graph Neural Network Approach

Forecasting retail sales accurately is important for supporting strategic, tactic, and operational decisions. Interdependencies among groups of time series are ubiquitous in real scenarios. However, previous research either completely ignores or fails to exploit these cross-series dependencies effectively and efficiently. This study follows the design science paradigm, and develops an innovative deep learning framework for long-term sales forecasting of retail drugstores. A novel graph neural network module is designed and developed for relational time series forecasting to capture cross-series (i.e., global) patterns. Moreover, we design a multi-source fusion module to fuse global patterns, specific temporal patterns of each time series, and context features. Experimental results demonstrate the effectiveness of our approach for both next-step and multi-step forecasting, and verify the utility of several crucial components in the framework. The proposed approach has important practical implications and research contributions

Apprentissage de représentations pour les données relationnelles

The increasing use of social and sensor networks generates a large quantity of data that can be represented as complex graphs. There are many tasks from information analysis, to prediction and retrieval one can imagine on those data where relation between graph nodes should be informative. In this thesis, we proposed different models for three different tasks: - Graph node classification - Relational time series forecasting - Collaborative filtering. All the proposed models use the representation learning framework in its deterministic or Gaussian variant. First, we proposed two algorithms for the heterogeneous graph labeling task, one using deterministic representations and the other one Gaussian representations. Contrary to other state of the art models, our solution is able to learn edge weights when learning simultaneously the representations and the classifiers. Second, we proposed an algorithm for relational time series forecasting where the observations are not only correlated inside each series, but also across the different series. We use Gaussian representations in this contribution. This was an opportunity to see in which way using Gaussian representations instead of deterministic ones was profitable. At last, we apply the Gaussian representation learning approach to the collaborative filtering task. This is a preliminary work to see if the properties of Gaussian representations found on the two previous tasks were also verified for the ranking one. The goal of this work was to then generalize the approach to more relational data and not only bipartite graphs between users and items.L'utilisation croissante des réseaux sociaux et de capteurs génère une grande quantité de données qui peuvent être représentées sous forme de graphiques complexes. Il y a de nombreuses tâches allant de l'analyse de l'information à la prédiction et à la récupération que l'on peut imaginer sur ces données où la relation entre les noeuds de graphes devrait être informative. Dans cette thèse, nous avons proposé différents modèles pour trois tâches différentes: - Classification des noeuds graphiques - Prévisions de séries temporelles relationnelles - Filtrage collaboratif. Tous les modèles proposés utilisent le cadre d'apprentissage de la représentation dans sa variante déterministe ou gaussienne. Dans un premier temps, nous avons proposé deux algorithmes pour la tâche de marquage de graphe hétérogène, l'un utilisant des représentations déterministes et l'autre des représentations gaussiennes. Contrairement à d'autres modèles de pointe, notre solution est capable d'apprendre les poids de bord lors de l'apprentissage simultané des représentations et des classificateurs. Deuxièmement, nous avons proposé un algorithme pour la prévision des séries chronologiques relationnelles où les observations sont non seulement corrélées à l'intérieur de chaque série, mais aussi entre les différentes séries. Nous utilisons des représentations gaussiennes dans cette contribution. C'était l'occasion de voir de quelle manière l'utilisation de représentations gaussiennes au lieu de représentations déterministes était profitable. Enfin, nous appliquons l'approche d'apprentissage de la représentation gaussienne à la tâche de filtrage collaboratif. Ceci est un travail préliminaire pour voir si les propriétés des représentations gaussiennes trouvées sur les deux tâches précédentes ont également été vérifiées pour le classement. L'objectif de ce travail était de généraliser ensuite l'approche à des données plus relationnelles et pas seulement des graphes bipartis entre les utilisateurs et les items