RichRelevance, a leader in experience personalization, announced the launch of first-of-its-kind ‘Deep Recommendations’, a set of advanced personalization technologies that, unlike traditional recommender engines, does not need historical events and behavioral data to immediately generate relevant product recommendations.
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The new approach solves two problems: (a) it removes constraints associated with traditional recommendations which don’t work for retailers and brands with sparse data – seasonal products, fast changing catalogs and long tail products, and (b) it helps product discovery by catching user’s preferences through a product’s visual features and textual description.
With Deep Recommendations, retailers and brands that regularly introduce new products can expose shoppers to these new products instantly. In addition, categories such as fashion and home furnishings where shoppers look for ‘visually similar’ or ‘visually complementary’ products can break through the clutter with highly relevant and high conversion visual AI based recommendations.
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RichRelevance Deep Recommendations are enabled by Xen AI, the most advanced machine learning engine in the space and the only one with composite deep learning, a industry first approach that blends all known data and decisions to predict the next best experience.
Xen AI extracts and combines feature vectors (the “DNA”) found in product text descriptions and catalog images, behavioral data, derived affinities and stated preferences and matches in real-time with shopper intent to create highly relevant, high-conversion recommendations. This helps your customers not only get what they are initially looking for, but also inspires them to discover contextual recommendations to fulfill their needs across their shopping journey.
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