B. John Oommen, Anis Yazidi, Ole-Christoffer Granmo, Journal of Information Processing Systems Vol. 8, No. 2, pp. 191-212, Jun. 2012  

https://doi.org/10.3745/JIPS.2012.8.2.191
Keywords: Weak es timators, User's Profiling, Time Varying Preferences
Fulltext: PDF

Abstract

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 a probability of 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 a 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 technology.

Statistics
Cite this article