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Who do you think I am? Interactive User Modelling with Item Metadata

Published: 13 September 2022 Publication History

Abstract

Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as “connecting relevant content to interested users”. Explanations have been found to help recommender systems achieve this goal by giving users a look under the hood that helps them understand why they are recommended certain items. Furthermore, explanations can be considered to be the first step towards interacting with the system. Indeed, for a user to give feedback and guide the system towards better understanding her preferences, it helps if the user has a better idea of what the system has already learned.
To this end, we propose a linear collaborative filtering recommendation model that builds user profiles within the domain of item metadata. Our method is hence inherently transparent and explainable. Moreover, since recommendations are computed as a linear function of item metadata and the interpretable user profile, our method seamlessly supports interactive recommendation. In other words, users can directly tweak the weights of the learned profile for more fine-grained browsing and discovery of content based on their current interests. We demonstrate the interactive aspect of this model in an online application for discovering cultural events in Belgium.

Supplementary Material

MP4 File (Who do you think I am? Interactive User Modelling with Item Metadata.mp4)
Demo video for the RecSys 2022 demo track paper "Who do you think I am? Interactive User Modelling with Item Metadata"

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
September 2022
743 pages
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Published: 13 September 2022

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Author Tags

  1. explainability
  2. interactive recommendation
  3. personalization
  4. transparency

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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