79 citations · 130 across the 16 of their papers we have counts for
8 papers · 1 filter
Enhancing the Patent Matching Capability of Large Language Models via the Memory Graph
Qiushi Xiong, Zhipeng Xu, Zhenghao Liu +6
Intellectual Property (IP) management involves strategically protecting and utilizing intellectual assets to enhance organizational innovation, competitiveness, and value creation.…
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation
Hao Deng, Haibo Xing, Kanefumi Matsuyama +8
Transformer-based sequential recommendation (TSR) models have shown superior performance in recommendation systems, where the quality of item representations plays a crucial role.…
Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction
Yi Xu, Zhiyuan Lu, Xiaochen Li +5
The Transformer has proven to be a significant approach in feature interaction for CTR prediction, achieving considerable success in previous works. However, it also presents poten…
LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction
Xin Song, Xiaochen Li, Jinxin Hu +5
With the rapid growth of user historical behavior data, user interest modeling has become a prominent aspect in Click-Through Rate (CTR) prediction, focusing on learning user inten…
Modeling User Viewing Flow Using Large Language Models for Article Recommendation
Zhenghao Liu, Zulong Chen, Moufeng Zhang +6
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-cli…
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
Qijie Shen, Hong Wen, Wanjie Tao +4
In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of p…
LHRM: A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform
Ziyi Wang, Wendong Xiao, Yu Li +2
Most current recommender systems used the historical behaviour data of user to predict user' preference. However, it is difficult to recommend items to new users accurately. To all…
Itinerary-aware Personalized Deep Matching at Fliggy
Jia Xu, Ziyi Wang, Zulong Chen +3
Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online…