Graph Data-Driven Recommendation Systems Empowered by Generative AI

Abstract: 

In our interconnected digital age, recommendation systems have emerged as vital tools in sectors offering a broad spectrum of products or services. Among these sectors, online travel companies face unique challenges and opportunities to enhance user experience and engagement. This showcase session unveils how we are using Generative AI to take a giant leap forward, revolutionizing how we approach these issues. Despite the remarkable strides made in recommendation systems over recent years, they often fall short when tasked with the complexities inherent in travel planning. Conventional systems, such as collaborative filtering or content-based filtering, struggle to encapsulate the diverse factors influencing individual user preferences, from potential destinations and activity types to distinct times of travel. Furthermore, these systems frequently overlook the social dynamics inherent in travel planning, failing to factor in user communities and the evolving landscape of travel trends. This shortfall culminates in a less-than-optimal user experience, leaving recommendations misaligned with user expectations and desires. Our solution seeks to redefine the status quo through a graph-based recommendation system. By harnessing the potent capacity of graph structures, we can capture the nuanced relationships between users, destinations, and activities with greater accuracy and finesse. Integral to this approach is the application of community detection algorithms, used to unearth clusters of users sharing similar travel interests, histories, and patterns. With these insights, we are poised to deliver personalized travel recommendations that align more closely with user inclinations. Moreover, we bring in a unique element by integrating a trend-based recommendation system. This system identifies and presents trending travel destinations or activities by analyzing real-time data, thereby ensuring that our recommendations are both current and relevant. Join us in this 20-minute showcase session as we delve into the design and implementation of our graph-based recommendation system. Discover how Generative AI, paired with innovative modelling, can transform a user's journey from decision to destination, creating personalized, timely, and engaging recommendations. This isn't just about enhancing user experience—it's about redefining how we traverse the world.

Bio: 

With a foundation in Computer Science, Ravi has spent over ten years as a Data Scientist and Machine Learning Engineer, specializing in AI and ML at the core of enterprise data solutions. His experience spans a variety of industries, including the Automobile, Banking, Retail, and Insurance sectors, delivering AI solutions globally. One of his standout accomplishments is designing a hyper-personalized recommendation system that boosted test drive bookings by an extraordinary 900%. Currently, as a Manager of Data Science, Ravi is focused on developing a scalable GenAI platform for generative content review across various formats of brand assets.

Open Data Science

 

 

 

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