Taxonomy-Oriented Recommendation towards Recommendation with Stage

In some E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Amazon within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms cannot effectively resolve the situation with a life cycle. In this paper, we model users’ behavior with life cycles by employing hand-crafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user’s long-term behavior using the item taxonomy, and then identify the exact stage of this user. By incorporating collaborative filtering into our method, we can easily provide a personalized item list to this user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Amazon demonstrates the efficacy of our proposed method.