This tutorial covers topics on multi-agentic recommender systems — recommender systems augmented with Large Language Models (LLMs) and multi-agent orchestration to enable multi-step reasoning, tool use, and interactive decision-making. The tutorial emphasizes foundational concepts, reusable design patterns, and practical lessons learned from large-scale e-commerce deployments. Specifically, we first cover background and recent trends in generative recommender systems and their connection to agentic approaches. We then survey major deployment areas in industry and review the agent orchestration frameworks developed to support them. Finally, we present a project walkthrough that traces the full lifecycle of an agentic recommender system, from scoping and data definition through modeling, deployment, and monitoring, to provide actionable deployment insights. The tutorial bridges perspectives from information retrieval (IR), recommender systems (RecSys), and large-scale industrial practice. The accompanying material can be found at agenticrecsys.github.io.

In recent years, massive transformer-based architectures have driven breakthrough performance in practical applications like autoregressive text-generation (LLMs) and click-prediction (recommenders). A common recipe for success is to train large models on massive web-scale datasets [3, 15], e.g., modern recommenders are trained on billions of user-item click events, and LLMs are trained on trillions of tokens extracted from the public internet. We are close to hitting the computational and economical limits of scaling up the size of these models, and we expect the next frontier of gains to come from improving the: (i) data quality of the training dataset, and (ii) data efficiency of the extremely expensive training procedure. Inspired by this shift, we present a set of “data-centric” techniques for recommendation and language models that summarizes a dataset into a terse data summary, which is both (i) high-quality, i.e., trains better quality models, and (ii) improves the data-efficiency of the overall training procedure. We propose techniques from two disparate data frameworks: (i) data selection (a.k.a., coreset construction) methods that sample portions of the dataset using grounded heuristics, and (ii) data distillation techniques that generate synthetic examples which are optimized to retain the signals needed for training high-quality models. Overall, this work sheds light on the challenges and opportunities offered by data optimization in web-scale systems, a particularly relevant focus as the recommendation community grapples with the grand challenge of leveraging LLMs.

Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities. Presently, the process of training massive models consisting of hundreds of millions to billions of parameters requires the extensive use of specialized hardware accelerators, such as GPUs, which are only accessible to a limited number of institutions with considerable financial resources. Moreover, there is often an alarming carbon footprint associated with training and deploying these models. In this paper, we take a step towards addressing these challenges by introducing BOLT, a sparse deep learning library for training large-scale search and recommendation models on standard CPU hardware. BOLT provides a flexible, high-level API for constructing models that will be familiar to users of existing popular DL frameworks. By automatically tuning specialized hyperparameters, BOLT also abstracts away the algorithmic details of sparse network training. We evaluate BOLT on a number of information retrieval tasks including product recommendations, text classification, graph neural networks, and personalization. We find that our proposed system achieves competitive performance with state-of-the-art techniques at a fraction of the cost and energy consumption and an order-of-magnitude faster inference time. BOLT has also been successfully deployed by multiple businesses to address critical problems, and we highlight one customer case study in the field of e-commerce.

"Garbage in, Garbage out" is a familiar maxim to ML practitioners and researchers, because the quality of a learned data representation is highly crucial to the quality of any ML model that consumes it as an input. To handle systems that serve billions of users at millions of queries per second (QPS), we need representation learning algorithms with significantly improved efficiency. At Google, we have dedicated thousands of iterations to develop a set of powerful techniques that efficiently learn high quality data representations. We have thoroughly validated these methods through offline evaluation, online A/B testing, and deployed these in over 50 models across major Google products. In this paper, we consider a generalized data representation learning problem that allows us to identify feature embeddings and crosses as common challenges. We propose two solutions, including: 1. Multi-size Unified Embedding to learn high-quality embeddings; and 2. Deep Cross Network V2 for learning effective feature crosses. We discuss the practical challenges we encountered and solutions we developed during deployment to production systems, compare with SOTA methods, and report offline and online experimental results. This work sheds light on the challenges and opportunities for developing next-gen algorithms for web-scale systems.

DNA sequencing, especially of microbial genomes and metagenomes, has been at the core of recent research advances in large-scale comparative genomics. The data deluge has resulted in exponential growth in genomic datasets over the past years and has shown no sign of slowing down. Several recent attempts have been made to tame the computational burden of sequence search on these terabyte and petabyte-scale datasets, including raw reads and assembled genomes. However, no known implementation provides both fast query and construction time, keeps the low false-positive requirement, and offers cheap storage of the data structure. We propose a data structure for search called RAMBO (Repeated And Merged BloOm Filter) which is significantly faster in query time than state-of-the-art genome indexing methods- COBS (Compact bit-sliced signature index), Sequence Bloom Trees, HowDeSBT, and SSBT. Furthermore, it supports insertion and query process parallelism, cheap updates for streaming inputs, has a zero false-negative rate, a low false-positive rate, and a small index size. RAMBO converts the search problem into set membership testing among K documents. Interestingly, it is a count-min sketch type arrangement of a membership testing utility (Bloom Filter in our case). The simplicity of the algorithm and embarrassingly parallel architecture allows us to stream and index a 170TB whole-genome sequence dataset in a mere 9 hours on a cluster of 100 nodes while competing methods require weeks.

Kernel density estimation is a simple and effective method that lies at the heart of many important machine learning applications. Unfortunately, kernel methods scale poorly for large, high dimensional datasets. Approximate kernel density estimation has a prohibitively high memory and computation cost, especially in the streaming setting. Recent sampling algorithms for high dimensional densities can reduce the computation cost but cannot operate online, while streaming algorithms cannot handle high dimensional datasets due to the curse of dimensionality. We propose RACE, an efficient sketching algorithm for kernel density estimation on high-dimensional streaming data. RACE compresses a set of N high dimensional vectors into tiny arrays of integer counters. These arrays are sufficient to estimate the kernel density for a large class of kernels. Our one-pass sketch is simple to implement and comes with strong theoretical guarantees. We evaluate our method on real-world high-dimensional datasets and show that our sketch achieves 10x better compression compared to existing methods.1