Learning Recommendations While Influencing Interests

TL;DR

This paper develops influence models for recommendation algorithms that adapt to evolving user interests, incorporating user influence into the learning process to optimize website recommendations.

Contribution

It introduces influence-aware models for recommendation systems, integrating user interest evolution into the learning algorithm for improved personalization.

Findings

Influence models affect steady user interests and optimal strategies.

A static optimization scheme for known parameters is developed.

A stochastic learning scheme adapts to unknown user profiles.

Abstract

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.