Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

TL;DR

This paper introduces a novel recommendation framework using a modified random walk approach to enhance diversity in personalized social media content, especially for political topics, without relying on bias labels.

Contribution

It develops a new random walk with erasure algorithm for diversifying recommendations and a model to accurately estimate ideological positions of users and content.

Findings

More ideologically diverse recommendations achieved

Effective in recommending long-tail items

Does not require bias labels for users or content

Abstract

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.