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Demo listing. This page uses illustrative seed metadata to demonstrate the Atlas. Claims shown here are not verified product facts.

Scalegate

Next-item recommendation that gates a recurrent cell with multi-scale convolutional features — capturing short- and longer-span item…

Next-item recommendation that gates a recurrent cell with multi-scale convolutional features — capturing short- and longer-span item unions in one sequential model.

DemoDesign onlyfazeZERO VerifiedProprietaryStrategy & Transformation

Last verified 15 Sept 2026 · Updated 23 Sept 2026 · Metadata v4

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Configuration + implementation engagement

Problem

Streaming media, retail, and content feeds where order matters and teams are choosing between pure RNN session models and convolutional sequence models (Caser-class) for next-item prediction.

Outcome

A design-complete foundation in the active pipeline — engineering scaffold planned to complete this month.

Who uses it

  • Operations Analyst
  • Solution Architect

Key capabilities

Capabilities are defined during customer configuration.

Typical workflow

Primary operating flow

Trigger: An operator starts the primary use case

  1. Task

    Stage 1: Capture inputs

    Operator

  2. AI-assisted

    Stage 2: Assist with draft

    System

  3. Human checkpoint

    Stage 3: Human approval

    Approver

Task Automated AI-assisted Human checkpoint Decision

About this foundation

Multi-scale Quasi-RNN next-item infrastructure. Markov/FPMC/Fossil-style sequential recommenders and single-span CNNs miss recurrent structure across *different* local n-gram scales. Scalegate (QR-Rec) uses convolutional filters of multiple widths as compositional gates inside a recurrent cell, so union-level features at several scales drive recurrence — SOTA on 15 datasets with absolute gains of roughly 0.57%–7.16% and relative 1.44%–17.65% on MAP, Recall@10, and NDCG@10 vs FPMC, Fossil, and Caser.

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