The RecSys Challenge 2025 focuses on developing robust recommendation systems capable of generalizing across multiple tasks in a highly sparse and dynamic user-behavior dataset. Designing unified user representations that generalize across multiple recommendation tasks under extreme sparsity remains a key challenge. In this paper, we present TFT-SR (Triple-Feature Transformer with Sparsity Regularization), a unified framework for behavioral modeling in the RecSys Challenge 2025. Our method fuses three complementary types of features-statistical descriptors, quantized temporal patterns, and hashed high-cardinality IDs-into a unified user vector. A dual-path neural encoder is used to separately extract dense and sparse representations, with the sparse branch regularized by an L1 penalty to promote interpretability and efficiency. Multi-task optimization [13, 15, 18] is performed through loss weighting, ensuring balanced learning across tasks. we participated in the competition under the team name 'xunzhou,' achieving 11th place on the final leaderboard and 5th place on the academic leaderboard.
Universal user representations hold the promise of reducing the need for task-specific modelling in personalized systems, enabling a single embedding to support a wide range of predictive tasks such as churn prediction, product propensity, and category-level recommendations. In this paper, we introduce BEHAV-E (Behavioural Embedding via Hybrid Action Variational Encoder), a self-supervised architecture that learns rich, 2048-dimensional user embeddings from multi-event behavioural data. BEHAV-E integrates both temporal and semantic signals by combining kernel density estimation (KDE) of user activity timelines, product and category embeddings via shared embedding bags, and an LSTM-based autoencoder for modelling search query semantics. We evaluate our approach within the RecSys Universal Behavioural modelling Challenge. Our results demonstrate that BEHAV-E effectively captures complex, multi-modal user behaviour in a compact and transferable embedding format. This paper provides an overview of the approach we used as team DArgk1 for the ACM RecSys Challenge 2025. Our submission achieved the 13..h rank and sum of scores of 4.5713 (borda count 483) in the competition academia-track final results. We release our source code at: https://github.com/knife982000/ RecSys2025Challenge
Traditional recommender systems are usually engineered for one of several objectives; predicting clicks, conversions, or churn depend on bespoke feature engineering that limits reuse and overlooks broader behavioral patterns. The RecSys Challenge 20251, organized by Synerise, tackles this shortcoming by introducing Universal Behavioral Profiles, a unified user representation framework meant to generalize across many downstream tasks. This paper presents the winning approach of the “rec2” team for this challenge. Our approach combines two Transformer-based embeddings with a compact statistical vector that summarizes coarse behavioral frequencies. This ensemble achieved consistently high accuracy on both the open tasks and the hidden tasks, highlighting its robust generalization ability. Our findings highlight two takeaways: (i) training Transformers with contrastive learning forms a particularly effective backbone for universal user embeddings; (ii) combining embeddings produced by diverse models yields an additional performance gains.
This work presents a sequence-aware framework for learning Universal Behavioral Profiles (UBPs) in the ACM RecSys Challenge 2025. Our method combines State-Space model-based sequential encoders (SS4Rec and dual Mamba4Rec models) with explicit churn tokens, action-specific loss weighting, temporal segmentation, and a rich set of 471 user-level aggregate features. Two complementary vocabularies balance high-frequency and low-frequency events, while a three-window scheme captures both short- and long-term dynamics. Evaluated on six downstream tasks (three open and three hidden) our team, Teletubbies, ranked 6th overall with sum of six scores of 4.73, achieving top-tier performance on five tasks but underperforming on the New Product Propensity (hidden2) cold-start task. An extensive ablation study confirms the unique contributions of each component to cross-task generalization. We analyze limitations in handling novel items and outline future work integrating side-information to address cold-start challenges.
