E-Learning personalization based on hybrid recommendation ...
Hybrid Recommendation Algorithm
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Hybrid Recommendation Engines : Factoring in transaction similarity, demographics, market and time conditions of the purchase • Select product category for analysis • Select customer type, conditions, demographics for analysis. • Stage 1: Product plane displays associations based on transaction • Bundling of products ~ clustering • Relative positioning of products ~ neighbours, bridge structures • Linkages among products ~ opportunities for cross sell and up sell • Isolated products ~ What is the opportunity ? • Stage 2 : Customer plane displays clusters based on past transactions, demographics, conditions of purchase •Stage 2 : Linking of customers to recommended products ~ combination of information retrieval and collaborative filtering via neural networks. •Separate treatment for low and high degree associations.
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Transcript of Hybrid Recommendation Algorithm
Hybrid Recommendation Engines : Factoring in transaction similarity, demographics, market and time conditions of the purchase
• Select product category for analysis
• Select customer type, conditions, demographics for analysis.
• Stage 1: Product plane displays associations based on transaction• Bundling of products ~ clustering• Relative positioning of products ~ neighbours, bridge structures• Linkages among products ~ opportunities for cross sell and up sell• Isolated products ~ What is the opportunity ?
• Stage 2 : Customer plane displays clusters based on past transactions, demographics, conditions of purchase
•Stage 2 : Linking of customers to recommended products ~ combination of information retrieval and collaborative filtering via neural networks.
•Separate treatment for low and high degree associations.•Stage 3 : Validation of recommendation engine
• Precision• Recall
Product Associations: Bundles, Bridge, Neighbours & Phrase
Product plane
Customer plane
Product Filters• Laptops• Mobile phones• AccessoriesCustomer Filters• Urban Cities – Tier 1 • High Income GroupCondition Filters•Pre Festival season• Mobile App purchases• Evening 6pm – 9 pm
Recommendations from customer plane to product plane
Product plane
Customer plane
Product Filters• Laptops• Mobile phones• AccessoriesCustomer Filters• Urban Cities – Tier 1 • High Income GroupCondition Filters•Pre Festival season• Mobile App purchases• Evening 6pm – 9 pm
Final Output
Customer ID ProductProbability of purchase Price point
Time point of analysis
A123 Moto G 55% 12,000Pre Diwali
2015
A123San Disk 6 GB
flash drive 87% 2500Pre Diwali
2015
A123Philips
Headphones 95% 3000Pre Diwali
2015
