Deep Learning Recommendation Model Expert Needed: Customer Purchase Prediction System

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DLRM Expert Needed: Multi-Modal Customer Purchase Prediction System Project Overview We need a Deep Learning Recommendation Model (DLRM) specialist to build an advanced customer purchase prediction system for our enterprise client. This is a challenging and exciting project that involves analyzing multi-platform behavioral data to predict customer purchasing likelihood. What We Need: Core Challenge Our enterprise client sells a specialized product and has many customers who have made purchases. We have rich behavioral data from multiple platforms (Facebook, LinkedIn, Spotify, Netflix) for these customers. Your task is to build a DLRM-based system that can predict whether the potential customer will purchase the product. Key Deliverables 1. Custom DLRM Architecture Design • Design a DLRM model specifically adapted for multi-modal data sources • Handle sparse categorical features (music genres, movie preferences) and dense numerical features (sentiment scores, engagement metrics) • Implement novel embedding strategies for cross-platform feature interactions • Create architecture documentation with technical justification for design choices 2. Multi-Modal Data Processing Pipeline • Build robust feature engineering pipeline for: o Facebook messages (NLP sentiment analysis, communication patterns) o LinkedIn posts (professional interests, networking behavior) o Spotify playlists (music preferences, listening patterns) o Netflix viewing history (content preferences, consumption patterns) • Implement feature standardization and normalization across platforms • Create unified embedding space for cross-platform interactions 3. DLRM Model Implementation • Custom DLRM architecture with modifications for multi-modal inputs • Advanced embedding techniques for categorical features • Attention mechanisms for feature importance weighting • Regularization strategies to prevent overfitting with small datasets • Ensemble methods combining multiple DLRM variants Required Skills and Expertise Must-Have Technical Skills • Deep Learning Recommendation Models (DLRM): Proven experience implementing and customizing DLRM architectures • PyTorch: Advanced proficiency in deep learning frameworks • Multi-Modal Learning: Experience combining different data modalities (text, categorical, numerical) • Embedding Techniques: Expertise in learned embeddings, especially for sparse categorical data • Feature Engineering: Advanced skills in processing social media and streaming platform data Highly Preferred Skills • Recommendation Systems: Production experience with large-scale recommendation engines • NLP: Experience with transformer models, sentiment analysis, and text classification • Time Series Analysis: Understanding of temporal patterns in user behavior • Privacy-Preserving ML: Knowledge of techniques for sensitive data handling Ideal Candidate Profile We're looking for someone who: • Has 3+ years of experience with production recommendation systems • Has published research or has demonstrable expertise in DLRM/deep learning for recommendations • Enjoys solving novel, challenging problems with limited data • Can balance theoretical knowledge with practical implementation skills • Has experience working with startups or fast-moving environments • Values code quality, documentation, and reproducible research Application Requirements: • DLRM Experience: Examples of previous recommendation system work • Multi-Modal Projects: Experience combining different data types • Research/Publications: Any relevant academic or industry publications

Keyword: Data Processing

Price: $30.0

Natural Language Processing PyTorch Recommendation System AI Development Recommendation Generation