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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-4503-8312-7/21/04 |
| Event Title | Proceedings of the Web Conference 2021 (WWW '2021) |
| Event Type | conference |
| Event Location | Ljubljana, Slovenia |
| Event Start Date | April 19 - 2021 |
| Event End Date | April 23 - 2021 |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery |
| Abstract Text | 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 random walks with erasure is able to generate more ideologically diverse recommenda- tions. 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. |
| Free access at | DOI |
| Digital Object Identifier | https://doi.org/10.1145/3442381.3449970 |
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