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Recommendation engine - sustainable product ordering service.pptx.pdf
- 1. Green products Recommendation engine How to help a user order the right dish at the right restaurant? With low latency and high precision
- 2. Sustainability Issues Social Capital Human Capital Business Model and Innovation Leadership and Governance Environment • Human Rights and Community relations • Customer Privacy • Data Security (GDPR) • Accessibility and Affordability • Product Quality and Safety • Customer Welfare • Sellking practices and Product Labelling • Labor practices • Employee Health and Safety • Employee Engagement • Diversity and Inclusion • Product design and Lifecycle Management • Business Model resilience • Supply chain management • Material Sourcing and Efficiency • Physical impacts of climate change • Business Ethics • Competetive Behavior • Management of the legal and regulatory environment • Critical Incident risk management • Systemic risk management • GHG emissions / CO2 , water footprint • Energy management • Waste management • Ecological impact Value drivers for today‘s customer
- 3. Features and dimensions 2. Context based learning (User metadata - age, gender, item metadata - cusine, ingredients seasonality) 1. Collaborative learning (User / Order similarity, higher data density, rating normalization to de-bias) Order History Restaurant History User Feed Our product evolution User persona User ratings Custom word embeddings Berlin based APIs Use-specific Features X Search frequency X Item Features X Weights X Updates to product strategy Historical Order Frequency X Itemized Sale history X Sorted by time X Itemized Order history X Current Search Pre processing Hyper -parameter tuning Model training Post Processing Evaluation Prenzlauerberg Model development pipeline Recomendations Objective Measure conversion of user search driven recommendations through to check out and order fulfillment How do we evaluate conversion? - Use hybrid models Sample model 1 Measure Weighted Precision and Weighted Recall Use Weighted F1 as a decision metric, where F1 = 2 X Precision X Recall / (Precision + Recall) Wedding Neukölln Sample model for creating recommendations Hybrid models
- 4. User Instance Infrastructure requirements on AWS to run reccomendation models Redis Lambda Click-Stream EC2 SNS Current Search Recomendations (Recos) Customer Data Order and Inventory Redshift Amazon DMS Schema 1 – product viewed, clicked, added and fulfilled Schema 2 – product detail, displayed price, search parameters Schema 3 – availability, delivery time, customization and preferences Generic Recos Add to carts Purchases Similar Products Cache Sniff out recommendations and possible ones with high probability of influencing customers Amazon S3 Fallback Recos Lambda consumer Redshift plays key role in Collating Schema and ETL workbook loads What is a reco? One set of probable recommendation that will support customer need, prioritization is still a business decision.