Characterizing Users and Tracking Their Activities in Online Classified Ads

Characterizing users and tracking their activities in online classified ads is a topic of great importance. However, some of the underlying problems associated with modeling users and detecting their behavioral changes have not been well-studied.

In this paper, we develop a probabilistic framework for characterizing users and quantifying some of the spatial and temporal variations in their posts. Our work on characterizing users study the problem in the context of detecting if a user belongs to a class, based on the ads the user has posted. Our approach is based on user profiling, where given statistics on user posts, the affinity of a user to a class is estimated. We show how profiles can be constructed with and without training data and report the effectiveness of our approaches in detecting two user classes business and non-business.

Our work on quantifying changes due to spatial and temporal variations is based on a probabilistic model of user behavior and a generative model that can predict ad posts from each location. We evaluate these models on a relatively large set of users and ads, and report our results on two classes of users monitored over a period of almost a year.