The Romanian market for plant protection products (PPP) is fragmented, dispersed, and very competitive. In recent years, there has been a constant decrease of farmers’ profitability, which has cascaded into the distribution of pesticides, fertilizers, and seeds. Since the structure of any market is dynamic over time, companies can determine the effectiveness of their different marketing strategies using analytical tools. As an alternative to econometric tools for predicting the market share in the farming industry, we propose the analytic network process (ANP) model, in which the market share is described as a network of nodes and clusters. Domain experts validate the ANP structure with respect to criteria and alternatives. The model allows the quantification of qualitative judgments provided by either experts or customers, through the highest eigenvalues. The eigenvalues are then further aggregated to deliver conclusive scores for the distribution of a particular market among competitors. The purpose of this research is twofold: (1) to develop an ANP-based tool for analyzing the competitive position (market share) of a company and (2) to help companies use the new tool in order to improve their business. The paper is of interest to PPP distributors, PPP manufacturers, customers, and policy-makers. The first two categories of stakeholders can use the analysis to better direct their marketing efforts, the customers can use it to select their providers, and the policy-makers can use it to evaluate and improve the control of PPP.

Last decades were characterised by a constant decline in the productivity of research and development activities of pharmaceutical companies. This is due to the fact that the drug discovery process contains an intrinsic risk that should be managed efficiently. Within this process, the early phase projects could be streamlined by doing more secondary research. These activities would involve the integration of chemical and biological knowledge from scientific literature in order to extract an overview and the evolution of a certain research area. This would then help refine the research and development operations. Considering the vast amount of pharmaceutical studies publications, it is not easy to identify the important information. For this task, a series of projects leveraged the advantages of the open pharmacological space through state-of-the-art technologies. The most popular are Knowledge Graphs methods. Although extremely useful, this technology requires increased investments of time and human resources. An alternative would be to develop a system that uses Natural Language Processing blocks. Still, there is no defined framework and reusable code template for the use-case of compounds development. In this study, it is presented the design and development of a system that uses Dynamic Topic Modelling and Named Entity Recognition modules in order to extract meaningful information from a large volume of unstructured texts. Moreover, the dynamic character of the topic modelling technique allows to analyse the evolution of different subject areas over time. In order to validate the system, a collection of articles from the Pharmaceutical Research Journal was used. Our results show that the system is able to identify the main research areas in the last 20 years, namely crystalline and amorphous systems, insulin resistance, paracellular permeability. Additionally, the evolution of the subjects is a highly valuable resource and should be used to get an in-depth understanding about the shifts that happened in a specific domain. However, a limitation of this system is that it cannot detect association between two concepts or entities if they are not involved in the same document.

With the recent COVID-19 pandemic, the world we knew changed significantly. The buying behavior shifted as well and is reflected by a growing transition to online interaction, higher media consumption and massive turn to online shopping. Companies that aim to remain top of mind to customers should ensure that their way of interacting with user is both relevant and highly adaptive. Companies should invest in state-of-the-art technologies that help manage and optimize the relationship with the client based on both online and offline data. One of the most popular applications that companies use to develop the client relationship is a Recommender System. The vast majority of traditional recommender systems consider the recommendation as a static procedure and focus either on a specific type of recommendation or on some limited data. In this paper, it is proposed a novel Reinforcement Learning-based recommender system that has an integrative view over data and recommendation landscape, as well as it is highly adaptive to changes in customer behavior, the Holistic Adaptive Recommender System (HARS). From system design to detailed activities, it was attempted to present a comprehensive way of designing and developing a HARS system for an e-commerce company use-case as well as giving a suite of metrics that could be used for its evaluation.

In the current fast paced and constantly changing environment, companies should ensure that their way of interacting with user is both relevant and highly adaptive. In order to stay competitive, companies should invest in state-ofthe-art technologies that optimize the relationship with the user using increasingly available data. The most popular applications used to develop user relationship are Recommender Systems. The vast majority of the traditional recommender system considers recommendation as a static procedure and focus on a specific type of recommendation, being not very agile in adapting to new situations. Also, when implementing a Recommender System there is the need to ensure fairness in the way decisions are made upon customer data. In this paper, it is proposed a novel Reinforcement Learning-based recommender system that is highly adaptive to changes in customer behavior and focuses on ensuring both producer and consumer fairness, Fairness Embedded Adaptive Recommender System (FEARS). The approach overcomes Reinforcement Learning's main drawback in recommendation area by using a small, but meaningful action space. Also, there are presented two fairness metrics, their calculation and adaptation for usage with Reinforcement Learning, this way ensuring that the system gets to the optimal trade-off between personalization and fairness.

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