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Memento framework boosts ad recommendations with personalized long-term data scaling

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces Memento, a personalized retrieval-augmented framework designed to improve recommendation systems by effectively scaling long-term user history data. Memento addresses challenges like attention dilution and catastrophic forgetting by treating historical user engagements as a document corpus and ad requests as queries, using Maximal Marginal Relevance (MMR) for retrieval. The framework includes two applications: Representation Memento for feature augmentation and Data Memento for multipass training. Memento demonstrates significant resource efficiency gains (5-10x) and low latency (sub-10ms), leading to a 1% CTR lift and 1.2% CVR lift in production for Facebook Feed and Reels, enabling personalization with over a year of user history. AI

IMPACT Enhances personalization in recommendation systems by effectively utilizing extensive historical user data, potentially improving user engagement and conversion rates.

RANK_REASON Publication of an academic paper detailing a new framework and its performance metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Memento framework boosts ad recommendations with personalized long-term data scaling

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Publication of an academic paper detailing a new framework and its performance metrics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sandeep Pandey ·

    Memento: Personalized RAG-Style Long-Retention Data Scaling for META Ads Recommendation

    Modeling of long history data suffers from long-context window attention dilution, system efficiency and catastrophic forgetting problems, where naive linear scaling approach like LastN would fail. We introduce Memento, a personalized retrieval-augmented framework that treats his…

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