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CAViaR: Context Aware Video Recommendations
In this paper, we propose a novel method which introduces diversity by modeling the impact of low diversity on user's engagement on individual items, thus being able to account for both diversity and relevance to adjust item scores.
CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems
Learning large-scale industrial recommender system models by fitting them to historical user interaction data makes them vulnerable to conformity bias.
PIE: Personalized Interest Exploration for Large-Scale Recommender Systems
We also analyze the value of exploration by defining relevant metrics around user-creator connections and understanding how this helps the overall recommendation pipeline with strong online gains in creator and ecosystem value.
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