Synerise AI/BigData Research | SAIR

Latest — Mar 20, 2025

The Evolution of the RecSys Challenge

🚀 Introduction Recommender systems have undergone significant evolution since their inception, gaining momentum after the landmark research paper published in 1994. The first research paper on recommender systems was published by Resnick, Iacovou, Suchak, Bergstrom, and Riedl. This field has expanded considerably over the years, driven by key challenges and competitions

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Enhancing Streaming and Request Handling Efficiency with gRPC and Coroutines: Part 3

Overview In previous articles, we primarily focused on the gateway side, where we handle requests from OQS and fetch the necessary data from workers. The gateway operates as both a client and a server. To manage the heavy workload of handling 2,160 workers with only 64 threads, we use

Enhancing Streaming and Request Handling Efficiency with gRPC and Coroutines: Part 2

Explore how to handle thousands of workers (2,160 and growing) using gRPC, coroutines, and asynchronous communication.

Synerise Introduces Cleora.ai 2.0: Redefining Entity Representation Learning

Synerise is thrilled to announce the release of Cleora.ai 2.0, the latest version of our cutting-edge, open-source solution for scalable and efficient entity representation learning.

Enhancing Streaming and Request Handling Efficiency with gRPC and Coroutines: Part 1

In this article, we dive deep into the technical challenges and solutions behind building a gateway server capable of handling millions of requests per minute.

AI for good: Cleora.AI created by Synerise in Biomedical Sciences.

Artificial Intelligence for drug development in medicine is very hot topic nowadays - which definitely will revolutionize this market segment in the next few years. How can we support this transformation by open sourcing top-notch AI solution which we created in Synerise? "Drug companies look to AI to end

BaseModel vs HSTU for sequential recommendations

In May 2024, a preprint titled "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" [1] was posted by Meta AI researchers to ArXiv. The preprint introduced a novel recommender, referred to as 'HSTU' which stands for “Hierarchical Sequential Transduction Units” promising new state-of-the-art

BaseModel vs TIGER for sequential recommendations

In December 2023, a paper titled "Recommender Systems with Generative Retrieval" [1] was presented by researchers at NeurIPS 2023. This paper introduced a novel recommender system, referred to as 'TIGER,' which garnered significant attention due to its innovative approach and state-of-the-art results in sequential recommendation tasks.

Temporal data splits

Understanding human behavior is a complex and challenging task. Behavioral modeling domain spanning tasks such as recommendation, propensity-to-buy, or LTV, require solutions tailored to domain's specific requirements. However, solutions are often copy-pasted from other domains. In behavioral modeling, we aim to predict future behaviors based on user'

EMDE vs Multiresolution Hash Encoding

When we created our EMDE algorithm we primarily had in mind the domain of behavioral profiling. For this reason, we were looking for a method to aggregate multiple events represented by various types of embeddings. However recently, a similar method was developed in the visual domain for two-dimensional data. Researchers