Information Retrieval

What if you could tune multimodal retrieval at serve time—trading accuracy, latency, and index size—simply by choosing how many learnable Meta Tokens (e.g., 1→16...

Yandex has introduced ARGUS (AutoRegressive Generative User Sequential modeling), a large-scale transformer-based framework for recommender systems that scales up to one billion parameters. This...

Retrieval-Augmented Generation (RAG) systems generally rely on dense embedding models that map queries and documents into fixed-dimensional vector spaces. While this approach has become...

Large Language Models (LLMs) have revolutionized many areas of natural language processing, but they still face critical limitations when dealing with up-to-date facts, domain-specific...

Personalized recommendations have become a vital component of many digital systems, aiming to surface content, products, or services that align with user preferences. The...

Search engines and recommender systems are essential in online content platforms nowadays. Traditional search methodologies focus on textual content, creating a critical gap in...

Multi-vector retrieval has emerged as a critical advancement in information retrieval, particularly with the adoption of transformer-based models. Unlike single-vector retrieval, which encodes queries...

Knowledge graphs have been used tremendously in the field of enterprise lately, with their applications realized in multiple data forms from legal persons to...

Out of the various methods employed in document search systems, "retrieve and rank" has gained quite some popularity. Using this method, the results of...

Scientific metadata in research literature holds immense significance, as highlighted by flourishing research in scientometrics—a discipline dedicated to analyzing scholarly literature. Metadata improves the...

Knowledge Retrieval systems have been prevalent for decades in many industries, such as healthcare, education, research, finance, etc. Their modern-day usage has integrated large...

Enhancing user experiences and boosting retention using recommendation systems is an effective and ever-evolving strategy used by many industries, such as e-commerce, streaming services,...

Recommendation systems are essential for connecting users with relevant content, products, or services. Dense retrieval methods have been a mainstay in this field, utilizing...

Sequential recommendation systems play a key role in creating personalized user experiences across various platforms, but they also face persistent challenges. Traditionally, these systems...

Sequential Recommendation systems have crucial applications in industries like e-commerce and streaming services. These systems collect and analyze the user interaction data over time...

The static knowledge base and hallucination-creating inaccuracy or fabrication of information are two common issues with large language models (LLMs). The parametric knowledge within...

Recommender systems are essential in modern digital platforms, enabling personalized user experiences by predicting preferences based on interaction data. These systems help users navigate...

Language and vision models have experienced remarkable breakthroughs with the advent of Transformer architecture. Models like BERT and GPT have revolutionized natural language processing,...

Recommender systems have been widely applied for studying user preferences; however, they face significant challenges in accurately capturing user preferences, particularly in the context...

The need for efficient retrieval methods from documents that are rich in both visuals and text has been a persistent challenge for researchers and...

Knowledge graphs are finding their way into financial practices, especially as powerful tools for competitor retrieval tasks. Graph's ability to organize and analyze complex...