Real-estate platforms represent a complex Information Retrieval (IR) environment where user relevance depends on high-dimensional intersections of location, time, and physical attributes. However, research into Geo-spatial Information Retrieval (GIR) and location-based recommendations is currently impeded by a lack of open, high-quality datasets that capture both the temporal evolution of markets and granular accessibility signals. To bridge this gap, we introduce RRE-GTC (Ru-Real-Estate Geo-Temporal Clusters), an open resource designed to benchmark ranking, pricing, and recommendation algorithms in dynamic spatial contexts. Derived from a massive collection of apartment listings in major Russian cities, RRE-GTC aggregates 456,182 geo-temporal clusters, offering a privacy-preserving alternative to releasing raw listing-level data. The dataset provides rich and distinct features: (i) geo-temporal price dynamics (stratified percentiles over time), (ii) detailed item metadata (building stock, supply volume), and (iii) transport accessibility signals (e.g., proximity, transport density). Uniquely, the resource includes a ''transport-isolated'' price signal derived from a linear attribution model, enabling novel research into attribute-based ranking and explainable search (e.g., separating ''location value'' from ''intrinsic quality''). Released under the Apache-2.0 license and hosted on Hugging Face, RRE-GTC lowers the barrier for experimenting with content-based recommendation and retrieval, spatiotemporal ranking, and market-aware search filtration.

Short-video recommendation presents unique challenges, such as modeling rapid user interest shifts from implicit feedback, but progress is constrained by a lack of large-scale open datasets that reflect real-world platform dynamics. To bridge this gap, we introduce the VK Large Short-Video Dataset (VK-LSVD), the largest publicly available industrial dataset of its kind. VK-LSVD offers an unprecedented scale of over 40 billion interactions from 10 million users and almost 20 million videos over six months, alongside rich features including content embeddings, diverse feedback signals, and contextual metadata. Our analysis supports the dataset's quality and diversity. The dataset's immediate impact is confirmed by its central role in the live VK RecSys Challenge 2025. VK-LSVD provides a vital, open dataset to use in building realistic benchmarks to accelerate research in sequential recommendation, cold-start scenarios, and next-generation recommender systems.

The optimization of bidding strategies for online advertising slot auctions presents a critical challenge across numerous digital marketplaces. A significant obstacle to the development, evaluation, and refinement of real-time autobidding algorithms is the scarcity of comprehensive datasets and standardized benchmarks. To address this deficiency, we present an auction benchmark encompassing the two most prevalent auction formats. We implement a series of robust baselines on a novel dataset, addressing the most salient Real-Time Bidding (RTB) problem domains: budget pacing uniformity and Cost Per Click (CPC) constraint optimization. This benchmark provides a user-friendly and intuitive framework for researchers and practitioners to develop and refine innovative autobidding algorithms, thereby facilitating advancements in the field of programmatic advertising. The implementation and additional resources can be accessed at the following repository https://github.com/avito-tech/bat-autobidding-benchmark, https://doi.org/10.5281/zenodo.14794182.