Recommendation Engine

Deliver Personalized Recommendations In Real-time with TigerGraph

Business Challenge
Traditional solutions
Deep Link Recommendation
Real-time Recommendation
AI and Machine Learning
Business Challenge
Every business faces an open-ended challenge: to maximize the revenue opportunity out of each interaction with every customer. Businesses offering a wide range of products or services face the additional challenge of matching the right product or service based on immediate browsing and search activity along with the historical data for the customer. Recommender systems are designed to tackle this business need. Their impact can be huge: Amazon, whose catalog size is unmatched, reports that 35% of its sales comes from cross-sell and up-sell recommendations.
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Moreover, the more personalized the recommendation, the greater the return. 82% of consumers report being influenced by a personalized shopping experience. The market for recommendation engine based on AI is expected to grow from USD 801.1 million in 2017 to USD 4414.8 million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period. Purchases with recommendation clicks result in a 10% higher average order value (AOV) and the per visit spend of a shopper who clicks a recommendation is five times higher. Now, more than ever, businesses are looking to capture the business moments into revenue and win market share with the personalized offers.
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Traditional Solutions Are Missing the Mark
Recommendation is a never-ending battle, for accuracy and timeliness. In today's hypercompetitive world with the sub-second response requirements, just having any old recommender isn't enough. Traditional recommenders perform global statistical computations offline, using snapshots of data that can be days old. They don't have the real-time modeling and nuanced profiling that is needed today.
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Real-time speed is essential. Website visitors have short attention spans: catch them now, or they're gone. For retailers, their product catalogs can change by the minute. Recommenders need to quickly understand the profile of their client, align that with the rapidly changing profiles of the larger customer base and product catalog, and produce engaging, personalized recommendations. Traditional recommendation engines built on the relational databases aren’t able to keep up with these requirements.
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Why TigerGraph, a Native Parallel Graph Database for Real-time Deep Link Recommendation Engine?
Deep Link Recommendation Engine
Using a graph for recommendation analytics is the first step towards faster and more personalized recommendations. Consider that the standard collaborative filtering algorithm is a 3-hop graph query: (People → who bought the Product ← which You just bought) → also bought these other Products But all graph databases are not created equal. TigerGraph easily handles 3 to 10+ hops, while many others struggle with more than 2 hops. TigerGraph's Deep Link Analytics let you customize and extend your analytics -- adding in hops to consider product features, customer demographics, and the context of the current situation -- which result in more accurate and more personalized recommendations.
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For example, It takes 2 hops to find similar shoppers: Shopper → (Demographics) → Similar Shoppers So demographic-aware collaborative filtering can be implemented in 5 hops: Shopper → Products purchased → Other Shoppers who purchased the product → Demographics → Other Shoppers that belong to the Demographics → Other Products Purchased. This is illustrated with the example, where Customer 1 has purchased a toy batmobile and light up shoes in prior visits. Customer 2 and Customer 3 have both purchased the batmobile toy, and belong to the demographics of “Suburban affluent parent”. Customer 4 belongs to that demographic as well and has purchased the video game “Super Mario Party for Nintendo Switch”. Based on the 5-hop analysis, the video game, “Super Mario Party for Nintendo Switch” is the recommended product for Customer 1.
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Real-time Recommendation Engine
TigerGraph's native parallel graph delivers not only more personalized results, but it does it in real-time. The result is the capture of key “Business Moments”, transient opportunities where people, businesses, data and "things" work together dynamically to create value. TigerGraph lets businesses capture these moments to personalize the customer experience which leads to more transactions.
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One of the leaders in mobile e-commerce uses TigerGraph to help drive online, personalized recommendations for its 300 million users. They use TigerGraph to model its catalog of millions of products from thousands of vendors, and its consumer data, including each shopper's real-time behavior on their website. TigerGraph increased the company's query speed by 100x compared to their previous solution and has helped it scale from a startup to Billions in annual revenue in just six years competing and winning against Amazon. The result is a better shopping experience with offers that are more likely to result in an initial sale, an up-sell, and cross- sells.
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Building the Next Generation Recommendation engine with AI and Machine Learning
Graphs with real-time deep link analytics are also powering the next generation of AI-based recommenders using machine learning to understand user behavior and preferred actions better than before. Have you ever made a major online purchase, such as a TV or washing machine, and then the recommender asks if you would like to buy a similar product? No! An intelligent recommender would recommend accessories instead.
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Similarly, an intelligent recommender can use the shopper's browsing behavior to guess at what stage a shopper is at: looking around, evaluating multiple options, price shopping, vacillating, etc. Then the recommender can make an appropriate suggestion to nudge the shopper along.
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Getting started with TigerGraph