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All existing mainstream search engines are built in the Web-2.0 era. They use brute-force keywords ranking to return results, it’s fast but not very intelligent. If you are trying to search the correlations between multiple entities, none of these search engines can satisfy you. Next-gen search for the Web- 3.0 era should be powered with real-time graph computing technologies so that search can be performed with augmented intelligence, speed, sense of causality, and clue of intention.

Significance

Graph-based Search & Recommendation has the following advantages:Truly smart search is possible, be it multiple keywords correlation or linkage search, causality search, or some other types of search traditional search can’t handle;Real-time recommendation is possible as real-time data refreshing is made possible;Working with Knowledge Graph, such as Merchandise Knowledge Graph, the recommendation is very much human like - 100% intelligent, instead of relying on pure aggregated statistical data results;Recommendation Graph = Real-time Merchandise Graph + Customer 360-degree Graph, it offers unified all-in-one recommendation solution.


If you are going to build a search and recommendation system that’s fast and smart, with low TCO and high ROI, adopting a graph-powered solution makes every sense, welcome to the rapidly evolving world of IT - graph is on the way to become main-stream.

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