User Behavior Analysis and Commodity Recommendation for Point-Earning App

摘要

The E-commerce website is well developed due to the internet become more popular for the past few year. It is a trend in recommendation system. It is the most important thing, how can we recommend the right thing for the right person at the right moment in the short time. On the other side, the first topic for the store is how can we give an exact information for the customers who needed. However, recommendation system depends on a huge data and analysis. It will be a big problem for a new company. This research data is from an App needs to collect point. We find out users’ profile and behavior from APP records and Facebook account. The App is mainly cooperate with a Taiwan makeup chained retailer, that it sells beauty, skin care products and some necessities, they have around 2,177 kinds of item. This kind of consumption recommended is different from the movie recommendation. Consumer product is related to the time and will be bought again by the user. In my paper, we select some user, who purchase times is high to resolve the data is not enough. Otherwise, through user feature, item feature, feature between with user and item and related to time. Using Machine Learning to train and built up the forecast model to predicted the testing data by the time. Resulting in F-measure from 0.0507 to 0.4413, Structure leaning with time greatly enhances the efficiency of our recommendation system.