Multi-Classes Feature Engineering with Sliding Window for Purchase Prediction in Mobile Commerce

Mobile devices become more and more prevalent in recent years, especially in young groups. The rapid progress of mobile devices promotes the development of M-Commerce business. The purchase on mobile terminals accounts for a considerable percentage in the total trading volume of E-Commerce and begins to draw the attention of E-Commerce corporation. Alibaba held a Mobile Recommendation Algorithm Competition aiming to recommend appropriate items for mobile users at the right time and place. The dataset provided by Alibaba consists of about 6 billion operation logs made by 5 million Taobao users towards over 150 million items spanning a period of one month. Compared with traditional scenarios in purchase predicting, the competition raised three challenges: (1)The dataset is too large to be processed in personal computers, (2)Some days with great discounts provided by Taobao Marketplace are within the period of dataset, (3)Positive samples are too few compared to the dimension of features. In this paper we study the problem of predicting the purchase behaviour of M-Commerce users, by exploring the solution for Alibaba's Mobile Recommendation Algorithm Competition. We first deeply study the habit of customers and filter many outliers. After that we adopt the method of "sliding window" to supply positive samples of training dataset and smooth the burst of sales near Dec 12th. We design a feature engineering framework to extract 6 categories of features that aim to capture the buying potential of user-item pairs. Our features exploit the interaction of user-item pair, user's shopping habit and item' attraction for users. Then we apply Gradient Boost Decision Trees (GBDT) as the training model. In the end, we combine outputs of individual GBDT together by Logistic Regression to get the final predictions. Our solution achieves 8.66% F1 score, and ranks the third place in the final round.