User behavior analysis and commodity recommendation for point earning apps
In recent years, due to the rapid development of e commerce, personalized recommendation systems have prevailed in product marketing. However, recommendation systems rely heavily on big data, creating a difficult situation for businesses at initial stages of development. We design several methods including a traditional classifier, heuristic scoring, and machine learning to build a recommendation system and integrate content based collaborative filtering for a hybrid recommendation system using Co Clustering with Augmented Matrices (CCAM) The source, which include users' persona from action taken in the app Facebook as well as product information derived from the web. For this particular app, more than 50% users have clicks less than 10 times in 1.5 year leading to insufficient data. Thus, we face the challenge of a cold start problem in analyzing user information. In order to obtain sufficient purchasing records, we analyzed frequent users and used web crawlers to enhance our item based data, resulting in F scores from 0.756 to 0.802. Heuristic scoring greatly enhances the efficiency of our recommendation system.
2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)