text: E-commerce web applications are almost ubiquitous in our day to day life, however as useful as they are, most of them have little to no adaptation to user needs, which in turn can cause both lower conversion rates as well as unsatisfied customers. We propose a machine learning system which learns the user behaviour from multiple previous sessions and predicts useful metrics for the current session. In turn, these metrics can be used by the applications to customize and better target the customer, which can mean anything from offering better offers of specific products, targeted notifications or placing smart ads. The data used for the learning algorithm is extracted from Google Analytics Enhanced E-commerce, which is enabled by most e-commerce websites and thus the system can be used by any such merchant. In order to learn the user patterns, only its behaviour features were used, which don't include names, gender or any other personal information that could identify the user. The learning model that was used is a double recurrent neural network which learns both intra-session and inter-session features. The model predicts for each session a probability score for each of the defined target classes.
  @cite_9: Predicting user behaviour on a website is a difficult task, which requires the integration of multiple sources of information, such as geo-location, user profile or web surfing history. In this paper we tackle the problem of predicting the user intent, based on the queries that were used to access a certain webpage. We make no additional assumptions, such as domain detection, device used or location, and only use the word information embedded in the given query. In order to build competitive classifiers, we label a small fraction of the EDI query intent prediction dataset edi-challenge-dataset , which is used as ground truth. Then, using various rule-based approaches, we automatically label the rest of the dataset, train the classifiers and evaluate the quality of the automatic labeling on the ground truth dataset. We used both recurrent and convolutional networks as the models, while representing the words in the query with multiple embedding methods.
  @cite_7: E-commerce has revolutionised how we browse and purchase products and services globally. However, with revolution comes disruption as retailers and users struggle to keep up with the pace of change. Retailers are increasingly using a varied number of machine learning techniques in areas such as information retrieval, user interface design, product catalogue curation and sentiment analysis, all of which must operate at scale and in near real-time. Understanding user purchase intent is important for a number of reasons. Buyers typically represent <5 of all e-commerce users, but contribute virtually all of the retailer profit. Merchants can cost-effectively target measures such as discounting, special offers or enhanced advertising at a buyer cohort - something that would be cost prohibitive if applied to all users. We used supervised classic machine learning and deep learning models to infer user purchase intent from their clickstreams. Our contribution is three-fold: first we conducted a detailed analysis of explicit features showing that four broad feature classes enable a classic model to infer user intent. Second, we constructed a deep learning model which recovers over 98 of the predictive power of a state-of-the-art approach. Last, we show that a standard word language deep model is not optimal for e-commerce clickstream analysis and propose a combined sampling and hidden state management strategy to improve the performance of deep models in the e-commerce domain. We also propose future work in order to build on the results obtained.
summary: Another classical problem is the task of Intent Prediction, which aims at finding the intention of a user during the current session. Recent methods include predicting if the user will buy during the current session using recurrent neural networks @cite_7 . Other approaches involve splitting the intent of a user in multiple disjoint classes, such as informational, transactional, considerational or navigational, corresponding to the marketing funnel.In @cite_9 , we have predicted these classes using only the search query that the user employed to land on the page. We used a semi supervised approach, by first creating an auto-labeling process and annotating a large amount of queries from a big corpus and train a partial model. Then, we fine tuned the model using a small subset of queries that were manually labeled, which proved better than using any of the steps individually.