arXiv Machine Learning
Aug 12
CADET: Context-Conditioned Ads CTR Prediction With a Decoder-Only Transformer
arXiv:2602. 11410v2 Announce Type: replace Abstract: Click-through rate (CTR) prediction is fundamental to online advertising systems.
Building a User Foundation Model for the Open Web
arXiv:2607. 28019v1 Announce Type: new Abstract: User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments where user identity is stable and persistent.
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arXiv Machine Learning
Jul 17
Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
arXiv:2607. 14161v1 Announce Type: cross Abstract: Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content.
arXiv AI
Sep 2
From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs
The paper introduces ReST, a recommendation‑native Transformer scaling framework designed to handle noisy, irregular, and sparsely supervised user behavior sequences in production ranking. ReST employs a dual‑gated attention encoder with rotary positional and temporal embeddings, and a lightweight cross decoder that decouples heavy encoding from fast decoding, enabling efficient compute‑once, decode‑many‑times ranking. Experiments on industrial and public benchmarks show that ReST outperforms traditional Transformer blocks, achieving higher accuracy and consistent scaling across sequence length, depth, and width, and a one‑week online A/B test on a production advertising platform yielded a 1.31% AUC lift and an 11.93% increase in a core revenue metric within a 50 ms P99 latency budget.
Hugging Face Trending Papers
Sep 2
Discriminative World Models for Web Agents
Discriminative World Models for Web Agents proposes a new training objective called predicted‑state matching, which forces a world model to produce representations that can distinguish the true resulting web state from those produced by alternative actions. The authors train these models on a branching dataset from WebArena Go‑Browse, where each decision point includes multiple actions and their outcomes. Experiments show that models trained with predicted‑state matching outperform those trained with standard supervised next‑state prediction on a held‑out benchmark, improve PRM‑style action ranking on WebPRMBench, and enhance end‑to‑end task success on WebArena‑Lite when used for test‑time action selection.
arXiv Machine Learning
Sep 17
Behavioral Fingerprinting and Navigation Prediction in Web Browsing
The study examines two behavioral inference tasks—session-level user identification and next-domain prediction—using large-scale anonymous web browsing traces. Classical and neural models are applied to user identification, while graph-based methods combined with Large Language Models (LLMs) are used for next-domain prediction. Results show that short browsing sessions are highly identifiable and future navigation is highly predictable, with LLM-derived semantic features offering only marginal improvements over structural and sequential models.
arXiv AI
Jun 26
From Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services Recommendations
arXiv:2606. 26277v1 Announce Type: cross Abstract: Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where pre-login web interactions and authenticated in-app experiences differ drastically.
arXiv AI
Sep 3
Discriminative World Models for Web Agents
The paper introduces Discriminative World Models for Web Agents, proposing a predicted-state matching objective that trains world models to produce representations that can distinguish the true resulting state from those of alternative actions. Using a branching dataset from WebArena Go-Browse, the authors demonstrate that this approach outperforms traditional supervised next-state prediction on a held‑out benchmark and improves action ranking on WebPRMBench. Additionally, employing the discriminative world model for test‑time action selection boosts end‑to‑end task success on WebArena‑Lite.
arXiv AI
3d ago
SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling
arXiv:2609.38397v1 Announce Type: new Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation....
arXiv Machine Learning
Jul 15
SlimPer: Make Personalization Model Slim and Smart
arXiv:2607. 12281v1 Announce Type: cross Abstract: Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length.
arXiv AI
Sep 10
SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching
SequenceO1 is an end‑to‑end framework that enables ultra‑long (up to 100K interactions) sequence modeling for recommendation systems. It compresses raw user histories into a fixed‑size sketch using Sketch Attention and then models short‑term and long‑term interests with Target‑to‑History Cross Attention. The system incorporates low‑rank caching, batching, pipeline lift, and a FlashSA kernel to keep training and inference efficient, achieving consistent offline and online performance gains when deployed at full traffic on Douyin.
arXiv AI
Jul 24
PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
arXiv:2607. 20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks.
arXiv Machine Learning
Jun 2
UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
arXiv:2606. 00422v1 Announce Type: cross Abstract: Modern recommendation systems predominantly train retrieval and ranking as separate models despite both increasingly relying on large transformers encoding the same user behavior data, duplicating parameters, compute, and serving cost.