Mitigating the Filter Bubble While Maintaining Relevance
Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin, Scott Sanner
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.