AN ENSEMBLE MODEL FOR CLICK THROUGH RATE PREDICTION

Internet has become the most prominent and accessible way to spread the news about an event or to pitch, advertise and sell a product, globally. The success of any advertisement campaign lies in reaching the right class of target audience and eventually convert them as potential customers in the future. Search engines like the Google, Yahoo, Bing are a few of the most used ones by the businesses to market their product. Apart from this, certain websites like the www.alibaba.com that has more traffic also offer services for B2B customers to set their advertisement campaign. The look of the advertisement, the maximum bill per day, the age and gender of the audience, the bid price for the position and the size of the advertisement are some of the key factors that are available for the businesses to tune. The businesses are predominantly charged based the number of clicks that they received for their advertisement while some websites also bill them with a fixed charge per billing cycle. This creates a necessity for the advertising platforms to analyze and study these influential factors to achieve the maximum possible gain through the advertisements. Additionally, it is equally important for the businesses to customize these factors rightly to achieve the maximum clicks. This research presents a click through rate prediction system that analyzes several of the factors mentioned above to predict if an advertisement will receive a click or not with improvements over the existing systems in terms of the sampling the data, the features used, and the methodologies handled to improve the accuracy. We used the ensemble model with weighted scheme and achieved an accuracy of 0.91 on a unit scale and predicted the probability for an advertisement to receive a click form the user

Machine learning approach for personalized recommendations on online platforms: uniplaces case study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe goal of this project is to develop a model to personalize the user recommendations of an online marketplace named Uniplaces. This online business offers properties for medium and long-term stays, where landlords can directly rent their place to customers (mainly students). Whenever a student makes a reservation, the booking must be approved by the property owner. The current acceptance rate is 25%. The model is a response to this low acceptance rate, and it will have to show to each student the properties that are more likely to be accepted by the landlord. As a secondary objective, the model seeks to identify the reasons behind the landlord’s decision to accept or reject bookings. The model will be constructed using information from the users, landlord and the property itself kindly provided by Uniplaces. This information will pre-process with data cleaning, transformation and features reduction (where two techniques were applied: dimensionality reduction, features selection). After the data processing, several models were applied to the normalized data. The predictive models that will be applied are already being used on other online markets and platforms like Airbnb, Netflix or LinkedIn, namely Support Vector Machine, Neural Networks, Decision Tree, Logistic Regression and Gradient Boosting. The probability of acceptance proved to be very easy to predict, all the models predict 100% of the test dataset when using the Principal Component Analysis as the Dimensionality Reduction technique. This can be explained mainly by the fact that the new calculated features have a strong correlation with the target variable. All the algorithms predict 100% of the target variable when using Principal Component Analysis as a technique of dimensionality reduction

Electronic Document Navigation Assistance Using Markings and/or Non-Uniform Scrolling

Generally, the present disclosure is directed to assisting in navigation within an electronic document. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a location within an electronic document to be marked based on user interaction with the electronic document

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection