Since a social network, by definition, is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary, estimating a user's interests, typically, involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning" capabilities of the estimator used. Therefore, resorting to strong estimators that converge with probability 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art.

Additional Metadata
Keywords Time Varying Preferences, User's Profiling, Weak estimators
Persistent URL dx.doi.org/10.1007/978-3-642-25832-9_81
Series Lecture Notes in Computer Science
Citation
Yazidi, A. (Anis), Granmo, O.-C. (Ole-Christoffer), & Oommen, J. (2011). Tracking the preferences of users using weak estimators. In Lecture Notes in Computer Science. doi:10.1007/978-3-642-25832-9_81