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Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems

Published: 07 July 2022 Publication History

Abstract

Online recommendation systems are prone to create filter bubbles, whereby users are only recommended content narrowly aligned with their historical interests. In the case of media recommendation, this can reinforce political polarization by recommending topical content (e.g., on the economy) at one extreme end of the political spectrum even though this topic has broad coverage from multiple political viewpoints that would provide a more balanced and informed perspective for the user. Historically, Maximal Marginal Relevance (MMR) has been used to diversify result lists and even mitigate filter bubbles, but suffers from three key drawbacks: (1)~MMR directly sacrifices relevance for diversity, (2)~MMR typically diversifies across all content and not just targeted dimensions (e.g., political polarization), and (3)~MMR is inefficient in practice due to the need to compute pairwise similarities between recommended items. To simultaneously address these limitations, we propose a novel methodology that trains Concept Activation Vectors (CAVs) for targeted topical dimensions (e.g., political polarization). We then modulate the latent embeddings of user preferences in a state-of-the-art VAE-based recommender system to diversify along the targeted dimension while preserving topical relevance across orthogonal dimensions. Our experiments show that our Targeted Diversification VAE-based Collaborative Filtering (TD-VAE-CF) methodology better preserves relevance of content to user preferences across a range of diversification levels in comparison to both untargeted and targeted variations of Maximum Marginal Relevance (MMR); TD-VAE-CF is also much more computationally efficient than the post-hoc re-ranking approach of MMR.

Supplementary Material

MP4 File (SIGIR2022_sp1992.mp4)
Presentation video on Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems. We present a novel targeted diversification method, TD-VAE-CF, which is built on top of VAE-CF. Targeted diversification is achieved by modulating the user latent representation towards the separation plane produced by Concept Activation Vectors (CAVs). The method outperforms the existing baseline, MMR, in both runtime and performance.

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 07 July 2022

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Author Tags

  1. diversity
  2. filter bubble
  3. recommendation systems

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  • (2024)AI Safety and SecurityChallenges in Large Language Model Development and AI Ethics10.4018/979-8-3693-3860-5.ch011(354-383)Online publication date: 30-Aug-2024
  • (2024)Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food DeliveryProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688119(643-653)Online publication date: 8-Oct-2024
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  • (2024)Modeling Variational Anchoring Effect for Recommender Systems2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00170(926-931)Online publication date: 25-Jun-2024
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  • (2023)DIPT: Diversified Personalized Transformer for QAC systems2023 13th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE60553.2023.10326229(019-023)Online publication date: 1-Nov-2023
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