Welcome to our podcast, where we delve into cutting-edge advancements in personalization! Today, we're highlighting MCM: A Multi-task Pre-trained Customer Model for Personalization, developed by Amazon LLC.

This innovative BERT-based model, with 10 million parameters, revolutionises how e-commerce platforms deeply understand customer preferences and shopping intents. Its novelty stems from significantly improving the state-of-the-art BERT4Rec framework by handling heterogeneous customer signals and implementing multi-task training. Key innovations include a random prefix augmentation method that avoids leaking future information and a task-aware attentional readout module that generates highly specific representations for different items and tasks.

MCM’s applications are extensive, empowering diverse personalization projects by providing accurate preference scores for recommendations, customer embeddings for transfer learning, and a pre-trained model for fine-tuning. It excels in next action prediction tasks, outperforming original BERT4Rec by 17%. While generally powerful, for highly specific behaviours like those driven by incentives, fine-tuning MCM with task-specific data can yield even greater improvements, driving over 60% uplift in conversion rates for incentive-based recommendations compared to baselines.

Discover how MCM is shaping the future of personalised e-commerce experiences!

Find the full paper here: https://assets.amazon.science/d7/a5/d17698634b70925612c07f07a0fa/mcm-a-multi-task-pre-trained-customer-model-for-personalization.pdf