7 papers
Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion
Yumou Liu, Zhenzhe Zheng, Jiang Rong +3
Modern content platforms offer paid promotion to mitigate cold start by allocating exposure via auctions. Our empirical analysis reveals a counterintuitive flaw in this paradigm: w…
Benchmark^2: Systematic Evaluation of LLM Benchmarks
Qi Qian, Chengsong Huang, Jingwen Xu +13
The rapid proliferation of benchmarks for evaluating large language models (LLMs) has created an urgent need for systematic methods to assess benchmark quality itself. We propose B…
ReMatch: Boosting Representation through Matching for Multimodal Retrieval
Qianying Liu, Xiao Liang, Zhiqiang Zhang +6
We present ReMatch, a framework that leverages the generative strength of MLLMs for multimodal retrieval. Previous approaches treated an MLLM as a simple encoder, ignoring its gene…
HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation
Jingyi Zhou, Cheng Chen, Kai Zuo +5
Large language models (LLMs) have recently demonstrated strong potential for sequential recommendation. However, current LLM-based approaches face critical limitations in modeling…
Cross-Scenario Unified Modeling of User Interests at Billion Scale
Manjie Xu, Cheng Chen, Xin Jia +9
User interests on content platforms are inherently diverse, manifesting through complex behavioral patterns across heterogeneous scenarios such as search, feed browsing, and conten…
RecLLM-R1: A Two-Stage Training Paradigm with Reinforcement Learning and Chain-of-Thought v1
Yu Xie, Xingkai Ren, Ying Qi +2
Traditional recommendation systems often grapple with "filter bubbles", underutilization of external knowledge, and a disconnect between model optimization and business policy iter…