Learning Recommendations While Influencing Interests
This addresses the limitation of rigid user interest assumptions in personalized recommendation systems, offering a method to account for influence, though it appears incremental by adapting existing models.
The paper tackles the problem of recommendation systems ignoring how recommendations influence user interests, developing influence models and a learning algorithm to optimize website suggestions. It analyzes the effect of these models on steady-state user interests and optimal strategies, showing differences compared to non-influential approaches.
Personalized recommendation systems (RS) are extensively used in many services. Many of these are based on learning algorithms where the RS uses the recommendation history and the user response to learn an optimal strategy. Further, these algorithms are based on the assumption that the user interests are rigid. Specifically, they do not account for the effect of learning strategy on the evolution of the user interests. In this paper we develop influence models for a learning algorithm that is used to optimally recommend websites to web users. We adapt the model of \cite{Ioannidis10} to include an item-dependent reward to the RS from the suggestions that are accepted by the user. For this we first develop a static optimisation scheme when all the parameters are known. Next we develop a stochastic approximation based learning scheme for the RS to learn the optimal strategy when the user profiles are not known. Finally, we describe several user-influence models for the learning algorithm and analyze their effect on the steady user interests and on the steady state optimal strategy as compared to that when the users are not influenced.