7 citations · 10 across the 13 of their papers we have counts for
10 papers · 1 filter
CroPS: Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search
Ao Xie, Jiahui Chen, Quanzhi Zhu +4
Dense retrieval has become a foundational paradigm in modern search systems, especially on short-video platforms. However, most industrial systems adopt a self-reinforcing training…
GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction
Zhijie Lin, Zhuofeng Li, Chenglei Dai +5
In a multi-stage recommendation system, reranking plays a crucial role in modeling intra-list correlations among items. A key challenge lies in exploring optimal sequences within t…
Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems
Yimeng Bai, Shunyu Zhang, Yang Zhang +5
Ranking models primarily focus on modeling the relative order of predictions while often neglecting the significance of the accuracy of their absolute values. However, accurate abs…
Unified Generative Search and Recommendation
Teng Shi, Jun Xu, Xiao Zhang +4
Modern commercial platforms typically offer both search and recommendation functionalities to serve diverse user needs, making joint modeling of these tasks an appealing direction.…
LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models
Yang Yan, Yihao Wang, Chi Zhang +8
Alongside the rapid development of Large Language Models (LLMs), there has been a notable increase in efforts to integrate LLM techniques in information retrieval (IR) and search e…
Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search
Wentian Bao, Hu Liu, Kai Zheng +5
Personalized search has been extensively studied in various applications, including web search, e-commerce, social networks, etc. With the soaring popularity of short-video platfor…
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models
Wuchao Li, Rui Huang, Haijun Zhao +10
Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. A…
A Self-boosted Framework for Calibrated Ranking
Shunyu Zhang, Hu Liu, Wentian Bao +2
Scale-calibrated ranking systems are ubiquitous in real-world applications nowadays, which pursue accurate ranking quality and calibrated probabilistic predictions simultaneously.…
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Jun Wang, Haoxuan Li, Chi Zhang +4
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCV…
Query-dominant User Interest Network for Large-Scale Search Ranking
Tong Guo, Xuanping Li, Haitao Yang +9
Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are var…