Since its inception, Marketing has always needed more clearly defined incrementality, i.e., a measurement of advertisement effectiveness. Nowadays, Marketing is an evergrowing business; within it, Digital Marketing is taking the spotlight. Digital Marketing brings multiple benefits, such as a global reach and a lower cost associated with customer communication. However, more importantly, customer interaction and engagement can be clearly tracked, which can help measure Marketing impact. Nowadays, this problem is tackled in two ways, A/B testing and attribution models. Even though statistically solid and proven, A/B testing, a form of hypothesis testing, faces implementation issues and other practical aspects, leading to only sometimes being used in real-world applications. On the other hand, Attribution models are not comparable, thus not quantifiable, and good attribution models are hard to develop, leaving companies relying on third-party providers. In short, this paper suggests that the impact of each marketing campaign can be measured in a two-step process: (1) Training a model to predict a customer's conversion, given their previous advertisement interactions; (2) Applying explainable machine learning methods to said model to infer the importance of each advertisement interaction in a user journey. The main methods used are permutation feature importance and Shapley values. The dataset is designed such that each type of advertisement interaction is a model's feature; thus, an importance value can be calculated for each interaction. On top of that, a local method - counterfactual explanations - and a possible implementation of a hyper-personal application are discussed. The proposed solution is shown to provide more accurate attributions than most common attribution models, with the possibility of augmenting the accuracy by changing the underlying model. It is also suggested that it could benefit significantly from more data on customer demographics, generating insights into how campaigns affect different customer segments. / Marknadsföring har sedan dess begynnelse alltid behövt en tydligare definition av inkrementalitet, det vill säga, mätningen av annonsens effektivitet. Marknadsföring är numera en ständigt växande verksamhet och inom den är det den digitala marknadsföringen som står i fokus. Digital marknadsföring ger flera fördelar t.ex. global räckvidd och lägre kostnader för kundkommunikation. Viktigare är dock att kundernas interaktion och engagemang kan spåras tydligt, detta bidrar i sig till att mäta marknadsföringens effektivitet. Det här problemet hanteras på två sätt: AB-testning och tilldelningsmodeller. Även om AB-testning är statistiskt sett både gedigen och beprövad leder oftast problem med genomförandet och andra praktiska aspekter till att det endast ibland används i korrekta tillämpningar. Å andra sidan är tillskrivningsmodeller inte jämförbara - de saknar mätbarhet - och det är svårt att utveckla bra tillskrivningsmodeller vilket gör att företagen förlitar sig på tredjepartsleverantörer. I korthet föreslår denna artikel att effekten av varje marknadsföringskampanj kan mätas i en tvåstegsprocess. (1) Träning av en modell för att förutsäga en kunds konvertering baserad på deras tidigare annonsinteraktioner. (2) Tillämpning av difinierade maskininlärningsmetoder på nämnda modeller för att härleda betydelsen av varje annonsinteraktion i en användares resa. De viktigaste metoderna som användes var permutation feature importance och Shapley-värden. Datamängden utformad så att varje typ av annonsinteraktion blir en modells funktion; på så sätt kan ett betydelsevärde beräknas för varje interaktion. Dessutom diskuteras en lokal metod - kontrafaktiska förklaringar - och ett möjligt genomförande av en hyperpersonlig applikation. Den föreslagna lösningen visade sig ge mer exakta tillskrivningar än de flesta vanliga tillskrivningsmodeller, med möjlighet att öka noggrannheten genom att ändra den underliggande modellen. Det föreslås också att den skulle kunna dra stor nytta av mer data om kundernas demografi, vilket skulle generera insikter om hur kampanjer påverkar olika kundsegment.

The integration of renewable energy sources (RES) into local energy distribution networks becomes increasingly important. Renewable energy highly depends on weather conditions, making it difficult to maintain stability in such networks. To still enable efficient planning and balancing, forecasts of energy supply are essential. However, typical distribution networks contain a variety of heterogeneous RES installations (e.g. wind, solar, water), each providing different characteristics and weather dependencies. Additionally, advanced meters, which allow the communication of final-granular production curves to the network operator, are not available at all RES sites. Despite these heterogeneities and missing measurements, reliable forecasts over the whole local distribution network have to be provided. This poses high challenges on choosing the right input parameters, statistical models and forecasting granularity (e.g. single RES installations vs. aggregated data). In this paper, we will discuss such problems in energy supply forecasting using a real-world scenario. Subsequently, we introduce our idea of a generalized optimization approach that determines the best forecasting strategy for a given scenario and sketch research challenges we are planning to investigate in future work.

Работа посвящена решению актуальной практической задачи структуризации текстов вакансий и извлечения из них информации о требуемых навыках для обеспечения возможности применения более эффективных алгоритмов поиска в коллекции документов. Задачи обработки текстов на естественном языке в настоящее время эффективно решаются с помощью методов машинного обучения, однако большая часть из них реализована в рамках конкретных технологий и языков. Поэтому в работе рассматривается вопрос создания отдельного веб-сервиса, реализующего функции обработки текстов вакансий с использованием библиотек для машинного обучения на языке Python и предоставляющего широкому кругу сторонних приложений возможность интеграции с ним посредством RESTful API интерфейса. Структуризация текстов вакансий выполняется с использованием регулярных выражений, кластеризации и классификации, причем извлекаются не только требования к навыкам соискателя, но также и выполняется выделение структуры объявления в виде разделов и заголовков к ним. / The paper is devoted to solving the actual practical problem of structuring job texts and extracting information about required skills from them to enable more efficient search algorithms in a collection of documents. Natural language text processing tasks are currently effectively solved using machine learning techniques, but most of them are implemented within specific technologies and languages. Therefore, this paper considers the creation of a separate web service that implements job text processing functions using Python machine learning libraries and provides a wide range of third-party applications with the ability to integrate with it via a RESTful API interface. Job texts are structured using regular expressions, clustering and classification, not only extracting the skill requirements of the job seeker, but also extracting the structure of the advertisement in the form of sections and their headings.

Au cours des dernières années, le domaine de la consommation a grandement évolué. Les agents de marketing ont commencé à utiliser l’Internet pour influencer les consommateurs en employant des tactiques originales et imaginatives qui ont rendus possible l’atteinte d'un niveau de communication interpersonnelle qui avait précédemment été insondable. Leurs interactions avec les consommateurs, en utilisant la technologie moderne, se manifeste sous plusieurs formes différentes qui sont toutes accompagnés de leur propre assortiment de problèmes juridiques. D’abord, il n'est pas rare pour les agents de marketing d’utiliser des outils qui leur permettent de suivre les actions des consommateurs dans le monde virtuel ainsi que dans le monde physique. Les renseignements personnels recueillis d'une telle manière sont souvent utilisés à des fins de publicité comportementale en ligne – une utilisation qui ne respecte pas toujours les limites du droit à la vie privée. Il est également devenu assez commun pour les agents de marketing d’utiliser les médias sociaux afin de converser avec les consommateurs. Ces forums ont aussi servi à la commission d’actes anticoncurrentiels, ainsi qu’à la diffusion de publicités fausses et trompeuses – deux pratiques qui sont interdites tant par la loi sur la concurrence que la loi sur la protection des consommateurs. Enfin, les agents de marketing utilisent diverses tactiques afin de joindre les consommateurs plus efficacement en utilisant diverses tactiques qui les rendent plus visible dans les moteurs de recherche sur Internet, dont certaines sont considérés comme malhonnêtes et pourraient présenter des problèmes dans les domaines du droit de la concurrence et du droit des marques de commerce. Ce mémoire offre une description détaillée des outils utilisés à des fins de marketing sur Internet, ainsi que de la manière dont ils sont utilisés. Il illustre par ailleurs les problèmes juridiques qui peuvent survenir à la suite de leur utilisation et définit le cadre législatif régissant l’utilisation de ces outils par les agents de marketing, pour enfin démontrer que les lois qui entrent en jeu dans de telles circonstances peuvent, en effet, se révéler bénéfiques pour ces derniers d'un point de vue économique. / The evolution of consumerism in recent years has been nothing short of remarkable. The unprecedented use of the Internet by marketers to influence consumers in original and imaginative ways has rendered possible a level of communicative efficiency that had previously been unfathomable. Their interaction with consumers using modern technology manifests itself in several different forms – all of which are accompanied by their own assortment of legal issues. To begin with, it is not unheard of for marketers to use tools meant to track the behaviour of individuals throughout both the virtual and physical worlds. The personal information collected in such a manner is often utilized for Online Behavioural Advertising purposes – a use which does not always respect the boundaries of privacy law. It has also become rather common for marketers to utilize online social media to promote conversations with consumers. It has occurred, however, that these forums have also been utilized to further the anti-competitive ambitions of companies while also serving as an outlet for false advertising – two eventualities that are prohibited by both competition laws and consumer protection laws. Finally, marketers utilize various tactics in order to more successfully reach consumers through online search engines – a practice known as Search Engine Marketing – some of which are considered to be dishonest and could present issues from both competition law and trademark law perspectives. This thesis essentially provides a detailed description of these tools and the manners in which they are utilized and then proceeds to illustrate the legal issues that may arise as a result of their use. In doing so, it outlines the legal boundaries within which marketers must use these tools so as to ultimately demonstrate that the laws that come into play under such circumstances may, in fact, prove to be beneficial to marketers from an economic perspective.

The flight search engine is a technology used in the air travel industry. It allows the traveler to search and book for the best flight options, such as the combination of flights while keeping the best services, options, and price. The computation for a flight search query can be very intensive given its parameters and complexity. The project goal is to predict the flight search queries computation cost for a new flight search engine product when dealing with parameters change and optimizations. The problem of flight search cost prediction is a regression problem. We propose to solve the problem by delimiting the problem based on its business logic and meaning. Our problem has data defined as a graph, which is why we have chosen Graph Neural Network. We have investigated multiple pretraining strategies for the evaluation of node embedding concerning a realworld regression task, including using a line graph for the training. The embeddings are used for downstream regression tasks. Our work is based on some stateoftheart Machine Learning, Deep Learning, and Graph Neural Network methods. We conclude that for some business use cases, the predictions are suitable for production use. In addition, the prediction of tree ensemble boosting methods produces negatives predictions which further degrade the R2 score by 4% because of the business meaning. The Deep Neural Network outperformed the most performing Machine Learning methods by 8% to 12% of R2 score. The Deep Neural Network also outperformed Deep Neural Network with pretrained node embedding from the Graph Neural Network methods by 11% to 17% R2 score. The Deep Neural Network achieved 93%, 81%, and 63% R2 score for each task with increasing difficulty. The training time range from 1 hour for Machine Learning models, 2 to 10 hours for Deep Learning models, and 8 to 24 hours for Deep Learning model for tabular data trained end to end with Graph Neural Network layers. The inference time is around 15 minutes. Finally, we found that using Graph Neural Network for the node regression task does not outperform Deep Neural Network. / Flygsökmotor är en teknik som används inom flygresebranschen. Den gör det möjligt för resenären att söka och boka de bästa flygalternativen, t.ex. kombinationer av flygningar med bästa service, alternativ och pris. Beräkningen av en flygsökning kan vara mycket intensiv med tanke på dess parametrar och komplexitet. Projektets mål är att förutsäga beräkningskostnaden för flygsökfrågor för en ny produkt för flygsökmotor när parametrar ändras och optimeringar görs. Problemet med att förutsäga kostnaderna för flygsökning är ett regressionsproblem. Vi föreslår att man löser problemet genom att avgränsa det utifrån dess affärslogik och innebörd. Vårt problem har data som definieras som en graf, vilket är anledningen till att vi har valt Graph Neural Network. Vi har undersökt flera förträningsstrategier för utvärdering av nodinbäddning när det gäller en regressionsuppgift från den verkliga världen, bland annat genom att använda ett linjediagram för träningen. Inbäddningarna används för regressionsuppgifter i efterföljande led. Vårt arbete bygger på några toppmoderna metoder för maskininlärning, djupinlärning och grafiska neurala nätverk. Vi drar slutsatsen att förutsägelserna är lämpliga för produktionsanvändning i vissa Vi drar slutsatsen att förutsägelserna är lämpliga för produktionsanvändning i vissa fall. Dessutom ger förutsägelserna från trädens ensemble av boostingmetoder negativa förutsägelser som ytterligare försämrar R2poängen med 4% på grund av affärsmässiga betydelser. Deep Neural Network överträffade de mest effektiva metoderna för maskininlärning med 812% av R2poängen. Det djupa neurala nätverket överträffade också det djupa neurala nätverket med förtränad node embedding från metoderna för grafiska neurala nätverk med 11 till 17% av R2poängen. Deep Neural Network uppnådde 93, 81 och 63% R2poäng för varje uppgift med stigande svårighetsgrad. Träningstiden varierar från 1 timme för maskininlärningsmodeller, 2 till 10 timmar för djupinlärningsmodeller och 8 till 24 timmar för djupinlärningsmodeller för tabelldata som tränats från början till slut med grafiska neurala nätverkslager. Inferenstiden är cirka 15 minuter. Slutligen fann vi  att användningen av Graph Neural Network för uppgiften om regression av noder inte överträffar Deep Neural Network.

Au cours des dernières années, le domaine de la consommation a grandement évolué. Les agents de marketing ont commencé à utiliser l’Internet pour influencer les consommateurs en employant des tactiques originales et imaginatives qui ont rendus possible l’atteinte d'un niveau de communication interpersonnelle qui avait précédemment été insondable. Leurs interactions avec les consommateurs, en utilisant la technologie moderne, se manifeste sous plusieurs formes différentes qui sont toutes accompagnés de leur propre assortiment de problèmes juridiques. D’abord, il n'est pas rare pour les agents de marketing d’utiliser des outils qui leur permettent de suivre les actions des consommateurs dans le monde virtuel ainsi que dans le monde physique. Les renseignements personnels recueillis d'une telle manière sont souvent utilisés à des fins de publicité comportementale en ligne – une utilisation qui ne respecte pas toujours les limites du droit à la vie privée. Il est également devenu assez commun pour les agents de marketing d’utiliser les médias sociaux afin de converser avec les consommateurs. Ces forums ont aussi servi à la commission d’actes anticoncurrentiels, ainsi qu’à la diffusion de publicités fausses et trompeuses – deux pratiques qui sont interdites tant par la loi sur la concurrence que la loi sur la protection des consommateurs. Enfin, les agents de marketing utilisent diverses tactiques afin de joindre les consommateurs plus efficacement en utilisant diverses tactiques qui les rendent plus visible dans les moteurs de recherche sur Internet, dont certaines sont considérés comme malhonnêtes et pourraient présenter des problèmes dans les domaines du droit de la concurrence et du droit des marques de commerce. Ce mémoire offre une description détaillée des outils utilisés à des fins de marketing sur Internet, ainsi que de la manière dont ils sont utilisés. Il illustre par ailleurs les problèmes juridiques qui peuvent survenir à la suite de leur utilisation et définit le cadre législatif régissant l’utilisation de ces outils par les agents de marketing, pour enfin démontrer que les lois qui entrent en jeu dans de telles circonstances peuvent, en effet, se révéler bénéfiques pour ces derniers d'un point de vue économique. / The evolution of consumerism in recent years has been nothing short of remarkable. The unprecedented use of the Internet by marketers to influence consumers in original and imaginative ways has rendered possible a level of communicative efficiency that had previously been unfathomable. Their interaction with consumers using modern technology manifests itself in several different forms – all of which are accompanied by their own assortment of legal issues. To begin with, it is not unheard of for marketers to use tools meant to track the behaviour of individuals throughout both the virtual and physical worlds. The personal information collected in such a manner is often utilized for Online Behavioural Advertising purposes – a use which does not always respect the boundaries of privacy law. It has also become rather common for marketers to utilize online social media to promote conversations with consumers. It has occurred, however, that these forums have also been utilized to further the anti-competitive ambitions of companies while also serving as an outlet for false advertising – two eventualities that are prohibited by both competition laws and consumer protection laws. Finally, marketers utilize various tactics in order to more successfully reach consumers through online search engines – a practice known as Search Engine Marketing – some of which are considered to be dishonest and could present issues from both competition law and trademark law perspectives. This thesis essentially provides a detailed description of these tools and the manners in which they are utilized and then proceeds to illustrate the legal issues that may arise as a result of their use. In doing so, it outlines the legal boundaries within which marketers must use these tools so as to ultimately demonstrate that the laws that come into play under such circumstances may, in fact, prove to be beneficial to marketers from an economic perspective.

[pt] O uso de robôs de busca para coletar informações para um determinado contexto sempre foi um problema desafiante e tem crescido substancialmente nos últimos anos. Por exemplo, robôs de busca podem ser utilizados para capturar dados de redes sociais profissionais. Em particular, tais redes permitem estudar as trajetórias profissionais dos egressos de uma universidade, e responder diversas perguntas, como por exemplo: Quanto tempo um ex-aluno da PUC-Rio leva para chegar a um cargo de relevância? No entanto, um problema de natureza comum a este cenário é a impossibilidade de coletar informações devido a sistemas de autenticação, impedindo um robô de busca de acessar determinadas páginas e conteúdos. Esta dissertação aborda uma solução para capturar dados, que contorna o problema de autenticação e automatiza o processo de coleta de dados. A solução proposta coleta dados de perfis de usuários de uma rede social profissional para armazenamento em banco de dados e posterior análise. A dissertação contempla ainda a possibilidade de adicionar diversas outras fontes de dados dando ênfase a uma estrutura de armazém de dados. / [en] The use of search bots to collect information for a given context has grown substantially in recent years. For example, search bots may be used to capture data from professional social networks. In particular, such social networks facilitate studying the professional trajectory of the alumni of a given university, and answer several questions such as: How long does a former student of PUC-Rio take to arrive at a management position? However, a common problem in this scenario is the inability to collect information due to authentication systems, preventing a search robot from accessing certain pages and content. This dissertation addresses a solution to capture data, which circumvents the authentication problem and automates the data collection process. The proposed solution collects data from user profiles for later database storage and analysis. The dissertation also contemplates the possibility of adding several other sources of data giving emphasis to a data warehouse structure.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This paper has as its main purpose to highlight some mathematical concepts, which are behind the ranking given by a research made on the website mostly used in the world: Google. At the beginning, we briefly approached some High School’s concepts, such as: Matrices, Linear Systems and Probability. After that, we presented some basic notions related to Directed Graphs and Markov Chains of Discrete Time. From this last one, we gave more emphasis to the Steady State Vector because it ensures foreknowledge results from long-term. These concepts are extremely important to our paper, because they will be used to explain the involvement of Mathematic behind the web search “Google”. Then, we tried to detail the ranking operation of the search pages on Google, i.e., how the results of a research are classified, determining which results are presented in a sequential way in order of relevance. Finally we obtained “PageRank”, an algorithm which creates what we call Google’s Matrices and ranks the pages of a search. We finished making a brief comment about the historical arising of the web searches, from their founders to the rise and hegemony of Google. / O presente trabalho tem como objetivo destacar alguns conceitos matemáticos que estão por trás do ranqueamento dado por uma pesquisa feita no site de busca mais usados do mundo, o “Google”. Inicialmente abordamos de forma breve alguns conteúdos da matemática do ensino médio, a exemplo de: matrizes, sistemas lineares, probabilidades. Em seguida são introduzidas noções básicas de grafos dirigidos e cadeias de Markov de tempo discreto; essa última, é dada uma ênfase ao vetor estado estacionário, por ele garantir resultados de previsão de longo prazo. Esses conceitos são de grande importância em nosso trabalho, pois serão usados para explicar o envolvimento da matemática por trás do site de buscas “Google”. Na sequência, buscamos detalhar o funcionamento do ranqueamento das páginas de uma busca no “Google”, isto é, como são classificados os resultados de uma pesquisa, determinando quais resultados serão apresentados de modo sequencial em ordem de relevância. Finalmente, chegamos na obtenção do “PageRank”, algoritmo que gera a chamada Matriz do Google e ranqueia as páginas de uma busca. Encerramos com um breve histórico do surgimento dos sites de buscas, desde os seus fundadores até a ascensão e hegemonia do Google.

This thesis focuses on the definition of important terms in e-commerce for the proper functioning e-shop. It analyzes the current condition of an existing e-shop and on the basis of the weaknesses proposes a partial strategy for further promotion of trade.

This bachelor thesis in applied mathematics and industrial engineering and management aimed to identify relationships between search parameters in flight comparison search engines and the exit conversion rate, while also investigating how the emergence of such comparison search engines has impacted the airline industry. To identify such relationships, several classification models were employed in conjunction with several sampling methods to produce a predictive model using the program R. To investigate the impact of the emergence of comparison websites, Porter's 5 forces and a SWOT - analysis were employed to analyze findings of a literature study and a qualitative interview. The classification models developed performed poorly with regards to several assessments metrics which suggested that there were little to no significance in the relationship between the search parameters investigated and exit conversion rate. Porter's 5 forces and the SWOT-analysis suggested that the competitive landscape of the airline industry has become more competitive and that airlines which do not manage to adapt to this changing market environment will experience decreasing profitability. / Detta kandidatexamensarbete inriktat på tillämpad matematik och industriell ekonomi syftade till att identifiera samband mellan sökparametrar från flygsökmotorer och konverteringsgraden för utträde till ett flygbolags hemsida, och samtidigt undersöka hur uppkomsten av flygsökmotorer har påverkat flygindustrin för flygbolag. För att identifiera sådana samband, tillämpades flera klassificeringsmodeller tillsammans med stickprovsmetoder för att bygga en predikativ modell i programmet R. För att undersöka påverkan av flygsökmotorer tillämpades Porters 5 krafter och SWOT-analys som teoretiska ramverk för att analysera information uppsamlad genom en litteraturstudie och en intervju. Klassificeringsmodellerna som byggdes presterade undermåligt med avseende på flera utvärderingsmått, vilket antydde att det fanns lite eller inget samband mellan de undersökta sökparametrarna och konverteringsgraden för utträde. Porters 5 krafter och SWOT-analysen visade att flygindustrin hade blivit mer konkurrensutsatt och att flygbolag som inte lyckas anpassa sig efter en omgivning i ändring kommer att uppleva minskande lönsamhet.