Predicting Product Duration for Adaptive Advertisement

Abstract

Whether or not the C2C customers would click the advertisement heavily relies on advertisement content relevance and customers’ searching progress. For example, when starting a purchasing task, customers are more likely to click the advertisements of their target products; while approaching the end, advertisements on accessories of the target products may interest the customers more. Therefore, the understanding of search progress on target products is very important in improving adaptive advertisement strategies. Search progress can be estimated by the time spent on the target product and the total time will be spent on this product. For the purpose of providing important information for product progress estimation, we propose a product duration prediction problem. Due to the similarities between the product duration prediction problem and user preference prediction problem (e.g. Large number of users, a history of past behaviors and ratings), the present work relies on the collaborative filtering method to estimate the searching duration of performing a purchasing task. Comparing neighbor-based, singular vector decomposition(SVD) and biased SVD method, we find biased SVD is superior to the others.