1 citations · 2 across the 8 of their papers we have counts for
9 papers
GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System
Jiping Liu, Zhongmin Zhang, Zisen Sang +7
Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effect…
DeGRe: Dense-supervised Generative Reranking for Recommendation
Chaotian Song, Jingyao Zhang, Chenghao Chen +6
In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in exploring optimal sequenc…
Pre-train and Fine-tune: Recommenders as Large Models
Zhenhao Jiang, Chenghao Chen, Hao Feng +5
In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommender…
Mobile Supply: The Last Piece of Jigsaw of Recommender System
Zhenhao Jiang, Biao Zeng, Hao Feng +4
Recommendation system is a fundamental functionality of online platforms. With the development of computing power of mobile phones, some researchers have deployed recommendation al…
Exploring the Spatiotemporal Features of Online Food Recommendation Service
Shaochuan Lin, Jiayan Pei, Taotao Zhou +3
Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner. The…
Multi-Granularity Attention Model for Group Recommendation
Jianye Ji, Jiayan Pei, Shaochuan Lin +4
Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics. Current studies have explored diff…
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
Zhenhao Jiang, Biao Zeng, Hao Feng +7
Large-scale online recommender system spreads all over the Internet being in charge of two basic tasks: Click-Through Rate (CTR) and Post-Click Conversion Rate (CVR) estimations. H…
BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service
Boya Du, Shaochuan Lin, Jiong Gao +6
Online Food Ordering Service (OFOS) is a popular location-based service that helps people to order what you want. Compared with traditional e-commerce recommendation systems, users…
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
Shaochuan Lin, Yicong Yu, Xiyu Ji +6
In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times…