7 papers · 1 filter
Probabilistic Residual Learning for Online Recommendations
Wenyuan Wang, Yusong Zhao, Zihao Xu +11
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffe…
LRanker: LLM Ranker for Massive Candidates
Tao Feng, Zijie Lei, Zhigang Hua +4
Large language models (LLMs) have recently shown strong potential for ranking by capturing semantic relevance and adapting across diverse domains, yet existing methods remain const…
UniRec: Unified Multimodal Encoding for LLM-Based Recommendations
Zijie Lei, Tao Feng, Zhigang Hua +5
Large language models have recently shown promise for multimodal recommendation, particularly with text and image inputs. Yet real-world recommendation signals extend far beyond th…
CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation
Tianxin Wei, Xuying Ning, Xuxing Chen +6
In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific items. However, existing generative recomme…
R1-Ranker: Teaching LLM Rankers to Reason
Tao Feng, Zhigang Hua, Zijie Lei +4
Large language models (LLMs) have recently shown strong reasoning abilities in domains like mathematics, coding, and scientific problem-solving, yet their potential for ranking tas…
Unified Semantic and ID Representation Learning for Deep Recommenders
Guanyu Lin, Zhigang Hua, Tao Feng +3
Effective recommendation is crucial for large-scale online platforms. Traditional recommendation systems primarily rely on ID tokens to uniquely identify items, which can effective…
Ads Supply Personalization via Doubly Robust Learning
Wei Shi, Chen Fu, Qi Xu +5
Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-…