Product-based Neural Networks for User Response Prediction

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201

Machine learning in digital marketing.

Marketers use machine learning to find patterns in user activities on a website or on a mobile application. This helps them predict the further behavior of users and quickly optimize advertising offers. In this paper, we present a novel algorithm based on Machine Learning used in the Information System for optimizing advertising services, attracting customers, growing sales, adapting the promotional offers that correspond to the hobbies of users, and for setting up a spam filter in the email or the Facebook service. Our framework demonstrates the feasibility of the approach to manage advertising campaigns to produce better results

OPTYMALIZACJA OFERT REKLAMOWYCH POPRZEZ UKIERUNKOWANIE W OPARCIU O SAMOUCZĄCĄ SIĘ BAZĘ DANYCH

The method of targeting advertising on Internet sites based on a structured self-learning database is considered. The database accumulates data on previously accepted requests to display ads from a closed auction, data on participation in the auction and the results of displaying ads – the presence of a click and product installation. The base is structured by streams with features – site, place, price. Each such structural stream has statistical properties that are much simpler compared to the general ad impression stream, which makes it possible to predict the effectiveness of advertising. The selection of bidding requests only promising in terms of the result allows to reduce the cost of displaying advertising.Rozważono metodę ukierunkowywania reklam w serwisach internetowych w oparciu o ustrukturyzowaną samouczącą się bazę danych. W bazie gromadzone są dane o wcześniej zaakceptowanych żądaniach wyświetlenia reklam z zamkniętej aukcji, dane o udziale w aukcji oraz o wynikach wyświetlania reklam – zarejestrowanie kliknięcia i instalacji produktu. Bazę tworzą strumienie z cechami – strona, miejsce, cena. Każdy taki strumień strukturalny ma właściwości statystyczne, które są znacznie prostsze w porównaniu do ogólnego strumienia wyświetleń reklamy, co pozwala przewidywać skuteczność reklamy. Selekcja tylko obiecujących pod względem wyniku zapytań ofertowych pozwala na obniżenie kosztów wyświetlania reklam

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

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