Paper Daily · Personalization (English)

Exploratory creative pre-ranking: Hierarchical empirical-Bayes Thompson sampling for real-time creative optimization

Thu, 29 Oct 2026 08:00:00 +0800

AI-generated content makes it easy for advertisers to produce many creatives, but each gets very few impressions. This Roblox paper adds a creative pre-ranking layer between recall and ad ranking. The historical play rate μ of a new creative's campaign, game and target audience serves as the prior mean, and the prior strength κ is computed in closed form from the variance of true differences amon…

Personalization · Score 4.75 · Deep dive · 30 min · Jiale Han, Ajith Moparthi, Dan Xie, Xiaowu Dai · Source

Rethinking Positional Encoding and Interaction Granularity for CTR Prediction

Thu, 29 Oct 2026 08:00:00 +0800

In unified Transformer CTR models like OneTrans, the authors tried six positional encodings on Kuaishou's KuaiRand data, and none beat using no positional encoding at all. Building on this, the Wuhan University and Tencent paper proposes TRIP (the authors' name for the whole method). Each behavior encodes both its rank in the sequence and how long before this prediction it happened. Behaviors are…

Personalization · Score 4.40 · Deep dive · 30 min · Xinxun Zhang, Yuxiang Wang, Xiaokai Zhou, Jiayue Li, Yi Li, Yue Liu, Jun Zhang, Xiaoling Zhou, Tiey… · Source

Can Complementary Signals Bridge Similarity Islands? Manifold-Augmented Graph Embedding for Multimodal Recommendation

Thu, 29 Oct 2026 08:00:00 +0800

Multimodal recommenders often build item graphs from content similarity, so similar substitutes connect tightly while complements that are bought together but look different (a camera and a memory card) stay isolated. MAGE works in two steps. Offline, it splits each item's co-purchased items by text similarity into substitutes and complements. The text vectors of the item and its substitutes form…

Personalization · Score 4.10 · Zhenye Yang, Jinpeng Chen, Wenbo Fu, Jun Ma, Huan Li, Qimin Zhou, Tao Wang, Li Sun, Senzhang Wang · Source

Compote: Collapse-Resistant Multiple Hypothesis Learning For Sequential Recommendation

Thu, 29 Oct 2026 08:00:00 +0800

A common problem in multi-interest retrieval is that the interest vectors end up pointing the same way. Criteo and Sorbonne's COMPOTE (the authors' name, short for collapse-resistant multiple-hypothesis learning) puts several prediction heads on a shared causal Transformer. Each sample splits its gradient across heads in proportion to the probability each gives the true item, raised to the power…

Personalization · Score 4.10 · Deep dive · 28 min · Louis Castres Saint Martin, Clement Calauzenes, Alain Rakotomamonjy, Patrick Gallinari · Source

BehaveRec: When Does Long-Term Behavioral History Help Sequential Recommendation? A Study with Session, History, and Community Signals

Thu, 29 Oct 2026 08:00:00 +0800

Are long-term history and community signals always useful? BehaveRec's answer is that it depends on how much history a user has. It combines three signals: a causal Transformer over the current session, a bidirectional encoder over 90 days of history, and a user graph built from behavioral similarity, with a gate deciding per user how much of each to use. On industrial data with over ten million…

Personalization · Score 4.10 · Aysenur Inan, Reza Yousefi Maragheh, Jayesh Uddhav Kudase, Selene Xu, Kaushiki Nag, Jianpeng Xu, Su… · Source

RELAY: One Query, Two Roles for Sequential Recommendation

Thu, 29 Oct 2026 08:00:00 +0800

In sequential recommendation, the part that represents user preference and the history used for scoring often look at different evidence. RELAY has one learned query attend over the history to form a preference representation, then reuses the same attention weights for two memories. Direct history boosts items the user already engaged with. Transition memory holds training-set counts of item b fo…

Personalization · Score 4.10 · Zesheng Li, Chengchang Pan, Honggang Qi · Source

Cross-Day Compatibility of Daily Item Embeddings: An Empirical Study of Alignment, Scale Management, and CTR Adaptation

Thu, 29 Oct 2026 08:00:00 +0800

Many teams retrain item co-occurrence embeddings daily and feed them to CTR models, but the same item's vector changes direction and length every day, so multi-day training data actually mixes several versions. This industrial empirical study compares many ways to handle it and reaches three conclusions. First, orthogonally aligning each new day's vectors to the previous day makes vectors more co…

Personalization · Score 4.05 · Yicheng Wang · Source

Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design

Thu, 29 Oct 2026 08:00:00 +0800

Meta's next-generation ads retrieval engine Andromeda brings near-ranking-quality models to retrieval, scoring every eligible ad. The new version replaces the single user tower with several specialized user towers as experts, and encodes the ad tower with real-time request context. An attention-based gate in the interaction layer activates only the most relevant experts per ad, cutting inference…

Personalization · Score 4.00 · Deep dive · 30 min · Source

DuTri: Dual-Branch Modeling with Triadic Factorization for Multimodal Multi-Scenario Recommendation

Thu, 29 Oct 2026 08:00:00 +0800

When multi-scenario recommenders add text and image information, they usually feed the vectors as ordinary features into the original parameter-sharing structure. DuTri's authors show with controlled experiments that this is not optimal, so they split the model in two. The collaborative branch applies CP decomposition to the three-way tensor of user, scenario and item interactions, yielding user,…

Personalization · Score 4.00 · Jintao Huang, Chaohua Yang, Kailiang Hao, Dugang Liu, Xing Tang, Zhong Ming · Source

Preserving rare-item recall under a storage budget: quantization, distillation, and the limits of teacher fidelity

Thu, 29 Oct 2026 08:00:00 +0800

With a fixed embedding storage budget, should you use a small full-precision model, a distilled student, or a higher-dimensional low-precision model? This paper compares several options with exactly the same byte count on six datasets, focusing on recall of rare items. The result is consistent. Factorizing a large teacher's user-by-item score matrix into fairly high-dimensional user and item vect…

Personalization · Score 3.85 · Han Chen, Yingrui Li · Source

Unified Heterogeneous Feature Interaction via Effective-Rank Scaling for Recommender Systems