Future of Data and AI: Vector Similarity Search panel discussion
The fascinating world of vector search has been the center of many discussions lately. Vector search has immense potential, with countless use cases from document retrieval to recommendation systems and across industries from e-commerce to financial services. During a panel discussion at the Future of Data and AI conference, Daniel Svonava, CEO of Superlinked, made the case how incorporating vector search can harness deep learning insights at scale.
Vector Similarity Search
Vector similarity search offers a more efficient and accurate approach to searching large datasets. By representing data as vectors and using similarity algorithms, search results can be refined and delivered quickly. This method can be applied to various types of data, from text and multimedia to images and recommendations, making it a versatile and powerful tool for enhancing user experiences.
Superlinked and Real-time ML Personalization
Daniel has firsthand experience with the power of vector search, building Superlinked, an infrastructure platform that helps evaluate, launch, and operate real-time ML personalization for consumer apps. Before founding Superlinked, Daniel was a tech lead at YouTube, where he built user modeling and ad performance prediction systems that guided the purchase process for over $10 billion of ad spend.
In the panel discussion Daniel, provides valuable insight into the realm of recommendation systems. Conventionally, when generating recommendations, one would vectorize the user's history and search for relevant content within a database. Superlinked takes this concept a step further by materializing user embeddings and creating an index of both content and users.
This advanced technique allows for better clustering of users, which, in turn, leads to more personalized recommendations. A critical aspect of this approach is having a database that can update the embeddings in real-time as users interact with the app, which requires efficient vector aggregation to represent the user's preferences.
Besides improving recommendations, clustering users based on their behavior can help identify and segment different user groups. One intriguing application of this is bot detection. By labeling a small percentage of users as spam accounts through human review, similar users can be automatically identified and labeled. This process forms the foundation for a general understanding of users and can result in more precise modeling.
Incorporating Vector Search into Your App
By incorporating vector search into your product, you can tap into the power of deep learning to deliver better user experiences. Vector search can be used to create more personalized recommendations, cluster users, and even detect bots. Its versatility and potential for customization make it an invaluable asset for developers and businesses alike.
Looking to harness the power of vector search? Let's discuss.
Watch the full panel discussion 👇