A Generational Shift in Recommendations
Suggest products like store assistants
Often the only way to sufficiently understand a user’s affinities is by looking at the products that interest them. No amount of tags or words can help to predict what they’re likely to buy. Visual AI uses deep learning to extract features from images, and generate recommendations that visually match the seed product. These recommendations help shoppers build conviction and make a decision, and serve both visually similar products, or complementary products that complete-the-look.
Personalize without historical and behavioral data
Traditional recommendation engines work well when you have tons of behavioral data for each product. New, seasonal and long tail products, however, do not get picked up in recommendations due to lack of past events. NLP powered recommendations go past this constraint, and start relating products immediately, so you can start personalizing from Day 1, and give shoppers the joy of discovering unique items.
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Personalize new and long-tail products immediately and improve relevancy with new deep learning AI techniques
Personalization traditionally works best when you have tons of behavioral data for each product. But how do you personalize for new products or ones that are considered “long-tail”?