Yamashita Haruka

Recently, there are various methods for activating data that is accumulated by e-commerce sites for the improvement the service quality. In this paper, we focus on the recommendation system and describe an approach, experiments, and future issues of the research that the author and students in the author’s laboratory have developed to improve the quality of service for a specific e-commerce site.

Online coupon distribution is a significant marketing measure that leads to increased sales. However, distributing coupons blindly risks lowering a company's profit ratio. It is, therefore, essential to estimate the coupon effect. In addition, users' potential purchase intention is thought to make a difference in the coupon effect. For example, users with low purchase intentions are likely to increase their gross profit through coupons. In contrast, users with high purchase intentions will likely decrease their gross profit through coupons. Therefore, it is possible to conduct highly effective targeting by analyzing the relationship between potential purchase intention and the coupon effect. In this study, we propose a framework containing an experimental design and a verification method based on machine learning to analyze the relationship between the coupon effect and the user's potential purchase intention. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data.

In the field of conventional recommendation systems, most of the models have been based on the prediction of evaluation values using evaluation value data directly assigned by users to their satisfaction with items. Recently, recommendation models that utilize behavioral history data such as implicit evaluation have been widely used. Neural Collaborative Ranking is a method for estimating and ranking the next most likely items to be observed in the list of items. Whereas, there are cases in which multiple implicit evaluations at different levels are observed, such as purchasing and browsing. However, the conventional NCR model cannot distinguish and learn multiple implicit evaluations, and cannot fully utilize the observed data. Therefore, in this study, we propose a model that takes into account multiple implicit evaluations with different levels in the NCR by adopting the method of Ding et al. In addition, we demonstrate the effectiveness of the proposed method.

Several studies have been proposed to analyze the factors that cause stock price fluctuations. It's known that utilizing textual data is effective in building models. In order to utilize text data in this method, it's necessary to quantify sentences using bag-of-words, etc., and at that time, concrete information is abstracted. Therefore, when it comes to investigating what specific factors caused the stock price fluctuation at a certain point in time, it's inappropriate to do so. In this case, it's desirable to construct a model using data other than textual data first, and then clarify the textual information to be focused on from the model. In this study, I first build a time-varying coefficient model using numerical data. Then, by focusing on the residuals of the model, we found the relationship between stock price fluctuations and specific factors expressed in text data at surrounding points in time.