Recommendation from access logs with ensemble learning

Many recommendation systems find similar users based on a profile of a target user and recommend products that he/she may be interested in. The profile is constructed with his/her purchase histories. However, histories of new customers are not stored and it is difficult to recommend products to them in the same fashion. The problem is called a cold start problem. We propose a recommendation method using access logs instead of purchase histories, because the access logs are gathered more easily than purchase histories and the access logs include much information on their interests. In this study, we construct user's profiles using product categories browsed by them from their access logs and predict products with Gradient Boosting Decision Tree. In addition, we carry out evaluation experiments using access logs in a real online shop and discuss performance of our proposed method comparing with conventional machine learning and Support Vector Machine (SVM). We confirmed that the proposed method achieved higher precision than SVM over 10 data sets. Especially, under unbalanced data sets, the proposed method is superior to SVM.