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Solution Accelerator

Recommendation Engines for Personalization

Pre-built code, sample data and step-by-step instructions ready to go in a Databricks notebook

recommendation engines for personalizations ui hex image

Increase conversion with personalized recommendations

Customers have different needs at each stage of the buyer journey. Choose the right recommender model for your scenario. Have a cold start problem? Try content-based recommenders. Nudging an existing customer to add to their cart? Wide-and-deep recommenders can help.

Image-based recommendations

Build a similarity-based image recommendation system for e-commerce that takes into account the visual similarity of items as an input for making product recommendations.

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Market-based recommendations

Build a recommender that leverages product affinities to suggest additional items.

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Wide-and-deep recommendations

Build a wide-and-deep recommender with collaborative filters that takes advantage of patterns of repeat purchases to suggest both previously purchased and related products.

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Matrix factorization (ALS) recommendations

Build a matrix factorization recommender to infer user ratings for various products. The alternating least squares (ALS) implementation for this recommender demonstrates patterns for matrix factorization that scale to accommodate the large numbers of user and product combinations found in real-world scenarios.

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Common sense recommendations with LLMs

Use this Solution Accelerator to develop product recommendations based on common sense linkages for new-to-market products and optimized recommendation engines using large language models.

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