Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce

Knowing if a user is a buyer vs window shopper solely based on clickstream data is of crucial importance for ecommerce platforms seeking to implement real-time accurate NBA (next best action) policies. However, due to the low frequency of conversion events and the noisiness of browsing data, classifying user sessions is very challenging. In this paper, we address the clickstream classification problem in the fashion industry and present three major contributions to the burgeoning field of AI in fashion: first, we collected, normalized and prepared a novel dataset of live shopping sessions from a major European e-commerce fashion website; second, we use the dataset to test in a controlled environment strong baselines and SOTA models from the literature; finally, we propose a new discriminative neural model that outperforms neural architectures recently proposed at Rakuten labs

Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection

Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce platforms to attract users to place orders. E-coupons are the digital equivalent of traditional paper coupons which provide customers with discounts or gifts. One of the fundamental problems related is how to deliver e-coupons with minimal cost while users' willingness to place an order is maximized. We call this problem the coupon allocation problem. This is a non-trivial problem since the number of regular users on a mature e-platform often reaches hundreds of millions and the types of e-coupons to be allocated are often multiple. The policy space is extremely large and the online allocation has to satisfy a budget constraint. Besides, one can never observe the responses of one user under different policies which increases the uncertainty of the policy making process. Previous work fails to deal with these challenges. In this paper, we decompose the coupon allocation task into two subtasks: the user intent detection task and the allocation task. Accordingly, we propose a two-stage solution: at the first stage (detection stage), we put forward a novel Instantaneous Intent Detection Network (IIDN) which takes the user-coupon features as input and predicts user real-time intents; at the second stage (allocation stage), we model the allocation problem as a Multiple-Choice Knapsack Problem (MCKP) and provide a computational efficient allocation method using the intents predicted at the detection stage. We conduct extensive online and offline experiments and the results show the superiority of our proposed framework, which has brought great profits to the platform and continues to function online

Towards preprocessing guidelines for neural network embedding of customer behavior in digital retail

Shopping transactions in digital retailing platforms enable retailers to understand customers’ needs for providing personalized experiences. Researchers started modeling transaction data through neural network embedding, which enables unsupervised learning of contextual similarities between attributes in shopping transactions. However, every study brings different approaches for embedding customer’s transactions, and clear preprocessing guidelines are missing. This paper reviews the recent literature of neural embedding for customer behavior and brings three main contributions. First, we provide a set of guidelines for preprocessing and modeling consumer transaction data to learn neural network embeddings. Second, it is introduced a multi-task Long Short-Term Memory Network to evaluate the guidelines proposed through the task of purchase behavior prediction. Third, we present a multi-contextual visualization of customer behavior embeddings, and its usefulness for purchase prediction and fraud detection applications. Results achieved illustrate accuracies above 40%, 60%, and 80% for predicting the next days, hours, and products purchased for some customers in a dataset composed of online grocery shopping transactions

Entre processus stochastiques et métriques d’évaluation : l’IA-créatrice à l'épreuve de l'étrangeté

The huge amount of data produced by the Big Tech in the last twenty years has contributed to the emergence of creative artificial intelligence as an automated tool that could allegedly produce works of art ex nihilo by itself. Only distantly reminiscent of the human brain, these algorithms produce pure statistical models by sifting through millions of images, videos or songs and identifying statistical correlations that occur between their constituents. As they lack historicity by nature, these systems are only able to generate novelty by relying on random number generators. This article explores the foundations and intricacies of what appears to be a paradigm of creation based solely on stochastic processes and statistical models. We specifically examine the hyperrational perspective from which engineers envision creation, how this perspective thrives on the assumption that the post-modern subject is void of all intrapsychic conflict, how it may catch on finally in an environment where normative-referenced evaluation has become paramount.The huge amount of data produced by the Big Tech in the last twenty years has contributed to the emergence of creative artificial intelligence as an automated tool that could allegedly produce works of art ex nihilo by itself. Only distantly reminiscent of the human brain, these algorithms produce pure statistical models by sifting through millions of images, videos or songs and identifying statistical correlations that occur between their constituents. As they lack historicity by nature, these systems are only able to generate novelty by relying on random number generators. This article explores the foundations and intricacies of what appears to be a paradigm of creation based solely on stochastic processes and statistical models. We specifically examine the hyperrational perspective from which engineers envision creation, how this perspective thrives on the assumption that the post-modern subject is void of all intrapsychic conflict, how it may catch on finally in an environment where normative-referenced evaluation has become paramount.The huge amount of data produced by the Big Tech in the last twenty years has contributed to the emergence of creative artificial intelligence as an automated tool that could allegedly produce works of art ex nihilo by itself. Only distantly reminiscent of the human brain, these algorithms produce pure statistical models by sifting through millions of images, videos or songs and identifying statistical correlations that occur between their constituents. As they lack historicity by nature, these systems are only able to generate novelty by relying on random number generators. This article explores the foundations and intricacies of what appears to be a paradigm of creation based solely on stochastic processes and statistical models. We specifically examine the hyperrational perspective from which engineers envision creation, how this perspective thrives on the assumption that the post-modern subject is void of all intrapsychic conflict, how it may catch on finally in an environment where normative-referenced evaluation has become paramount

Effects of Severe Data Imbalance on Evaluation of Support Vector Machines and Decision Trees

Data imbalance refers to a phenomena when one of the classes is much better represented in the dataset compared to the others. Many researchers have been facing data imbalances in various fields, including medicine, fraud or device failure detection, and predicting conversions from user behavior data. Even though there exist a significant number of papers devoted to predicting user behavior, a closer analysis of data imbalances and how they affect performance of classifiers and model evaluation is missing in this literature. This thesis attempts to fill this gap. In this thesis work, support vector machines and decision trees are employed to predict whether a website user is interested in making a purchase of a certain product or not. Each of the classifiers is evaluated using four strategies: balanced training and testing data, balanced training and unbalanced testing data, unbalanced training and balanced testing data, unbalanced training and testing data. The metrics used for models’ performance evaluation are: Accuracy, Precision, F1, MCC, Sensitivity, Specificity and ROC-AUC. The learning curves are built for each of the metrics to evaluate how performance changes when training sample size increases. Hierarchical clustering is applied to evaluate how dimensionality reduction affects the performance. Predictions yielded by the classifiers are to be used by a company to target marketing efforts. In this thesis work, an emphasis is put on utility measures and how they can be used to evaluate and compare the usefulness of the classifiers for the marketing tas