Search engines and recommender systems are essential in online content platforms nowadays. Traditional search methodologies focus on textual content, creating […]
Category: Information Retrieval
Researchers from Stanford, UC Berkeley and ETH Zurich Introduces WARP: An Efficient Multi-Vector Retrieval Engine for Faster and Scalable Search
Multi-vector retrieval has emerged as a critical advancement in information retrieval, particularly with the adoption of transformer-based models. Unlike single-vector […]
Baidu Research Introduces EICopilot: An Intelligent Agent-based Chatbot to Retrieve and Interpret Enterprise Information from Massive Graph Databases
Knowledge graphs have been used tremendously in the field of enterprise lately, with their applications realized in multiple data forms […]
SlideGar: A Novel AI Approach to Use LLMs in Retrieval Reranking, Solving the Challenge of Bound Recall
Out of the various methods employed in document search systems, “retrieve and rank” has gained quite some popularity. Using this […]
This AI Study Saves Researchers from Metadata Chaos with a Comparative Analysis of Extraction Techniques for Scholarly Documents
Scientific metadata in research literature holds immense significance, as highlighted by flourishing research in scientometrics—a discipline dedicated to analyzing scholarly […]
This AI Research Developed a Question-Answering System based on Retrieval-Augmented Generation (RAG) Using Chinese Wikipedia and Lawbank as Retrieval Sources
Knowledge Retrieval systems have been prevalent for decades in many industries, such as healthcare, education, research, finance, etc. Their modern-day […]
AutoGraph: An Automatic Graph Construction Framework based on LLMs for Recommendation
Enhancing user experiences and boosting retention using recommendation systems is an effective and ever-evolving strategy used by many industries, such […]
Meta AI Proposes LIGER: A Novel AI Method that Synergistically Combines the Strengths of Dense and Generative Retrieval to Significantly Enhance the Performance of Generative Retrieval
Recommendation systems are essential for connecting users with relevant content, products, or services. Dense retrieval methods have been a mainstay […]
Meta AI Introduces a Paradigm Called ‘Preference Discerning’ Supported by a Generative Retrieval Model Named ‘Mender’
Sequential recommendation systems play a key role in creating personalized user experiences across various platforms, but they also face persistent […]