The RecSys Challenge 2025 promotes a unified approach to behavior modeling by introducing Universal Behavioral Profiles. These user representations encode essential aspects of past interactions and are designed for universal applicability across different downstream tasks, thereby promoting generalization across applications and addressing the need for portable and efficient recommender systems. The participants task was to create universal user embeddings from detailed e-commerce activity logs. These embeddings were then fed into a small neural network to predict customer behavior in subsequent timeframes. The provided challenge dataset was large and sparse, requiring innovative methods to leverage the available interaction data in an effective way. Overall, the challenge was highly attractive with 400 teams participating in the competition.

Behavioral modeling is an emerging machine learning area which aims to predict user actions, especially in commercial settings. Companies actively gather data such as clicks, likes, page views, card transactions, add-to-basket, or purchase events. However, the large size of data combined with hardships in applying sophisticated graph-based machine learning often leads to data not being used for modeling at all. In response to this, we propose the Monad platform. The primary focus of Monad is to train a large, private behavioral foundation model for each client company. The foundation model is then fine-tuned to any user behavioral prediction task, such as recommendations or churn. The Monad foundation models are based on our algorithms – Cleora and EMDE – which are award winning solutions (KDD Cup and other anonymized AI contests). Cleora and EMDE allow to process real-life datasets composed of billions of events in record time. In this paper, we present and analyze the temporal side of Monad. Time is a crucial aspect of behavioral training, because many target tasks such as propensity to buy rely on seasonal aspects and predictions are usually required to include a time frame (e.g. “Will the user buy brand X within a week from now?”). To tackle this problem, we present the concept of time-based data splits in training, as well as approaches towards time-based feature encoding, which do not require normalization or any feature statistics.

The complexity of industry-grade event-based datalakes grows dynamically each passing hour. Companies actively gather behavioral information on their customers, recording multiple types of events, such as clicks, likes, page views, card transactions, add-to-basket, or purchase events. In response to this, the Synerise Monad platform has been proposed. The primary focus of Monad is to produce Universal Behavioral Representations (UBRs) - large vectors encapsulating the behavioral patterns of each user. UBRs do not lose knowledge about individual events, in contrast to aggregated features or averaged embeddings. They are based on award-winning algorithms developed at Synerise - Cleora and EMDE - and allow to process real-life datasets composed of billions of events in record time. In this paper, we introduce a new aspect of Monad: private foundation models for behavioral data, trained on top of UBRs. The foundation models are trained in purely self-supervised manner and allow to exploit general knowledge about human behavior, which proves especially useful when multiple downstream models must be trained and time constraints are tight, or when labeled data is scarce. Experimental results show that the Monad foundation models can cut training time in half and require 3x less data to reach optimal results, often achieving state-of-the-art results.

The growth of time-sensitive heterogeneous data in industry-grade datalakes has recently reached unprecedented momentum. In response to this, we propose Synerise Monad - a prototype of a real-time behavioral modeling platform for event-based data streams. It automates representation learning and model training on massive data sources with arbitrary data structures. With Monad we showcase how to automatically process various data modalities, such as temporal, graph, categorical, decimal, and textual data types, in a time-sensitive way allowing for real-time time feature creation and predictions. Monad's distributed and scalable architecture coupled with efficient award-winning algorithms developed at Synerise - Cleora and EMDE - allows to process real-life datasets composed of billions of events in record time. The Monad ecosystem showcases a viable path towards real-time event-based AutoML.

Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, om-nichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data availability, representing transactions, users, and background product information. Finding products related in different ways, i.e., substitutes and complements is essential for users' recommendations at the vendor's site and for the vendor - to perform efficient assortment optimization. The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm. We also provide its experimental evaluation with regards to the state-of-the-art Shopper algorithm, studying the relevance of recommendations with surveys from industry experts. It is concluded that the new approach presented here offers suitable choices of recommended products, requiring a minimal amount of additional information. The algorithm can be used in various enterprises, effectively identifying substitute and complementary product options.

Fashion retrieval is the challenging task of finding an exact match for fashion items contained within an image. Difficulties arise from the fine-grained nature of clothing items, very large intra-class and inter-class variance. Additionally, query and source images for the task usually come from different domains - street photos and catalogue photos respectively. Due to these differences, a significant gap in quality, lighting, contrast, background clutter and item presentation exists between domains. As a result, fashion retrieval is an active field of research both in academia and the industry. Inspired by recent advancements in Person Re-Identification research, we adapt leading ReID models to be used in fashion retrieval tasks. We introduce a simple baseline model for fashion retrieval, significantly outperforming previous state-of-the-art results despite a much simpler architecture. We conduct in-depth experiments on Street2Shop and DeepFashion datasets and validate our results. Finally, we propose a cross-domain (cross-dataset) evaluation method to test the robustness of fashion retrieval models.