9 papers
RecoReward: Recommender-Guided Multimodal Description Generation for Recommendation
Guohong Mu, Yueyang Liu, Jiangxia Cao +8
Multimodal large language models (MLLMs) can convert multimodal item content into structured descriptions used as semantic features for recommendation. Conventional content-only ge…
Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence
Ruochen Yang, Shuang Wen, Pengbo Xu +6
Modern industrial recommendation systems typically separate recall and ranking into two independent stages. Although this cascade supports corpus-level retrieval and fine-grained m…
From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
Ruochen Yang, Xiaodong Li, Jiawei Sheng +6
Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under…
MaRI: Accelerating Ranking Model Inference via Structural Re-parameterization in Large Scale Recommendation System
Yusheng Huang, Pengbo Xu, Shen Wang +7
Ranking models, i.e., coarse-ranking and fine-ranking models, serve as core components in large-scale recommendation systems, responsible for scoring massive item candidates based…
OnePiece: The Great Route to Generative Recommendation -- A Case Study from Tencent Algorithm Competition
Jiangxia Cao, Shuo Yang, Zijun Wang +1
In past years, the OpenAI's Scaling-Laws shows the amazing intelligence with the next-token prediction paradigm in neural language modeling, which pointing out a free-lunch way to…
RecCoT: Enhancing Recommendation via Chain-of-Thought
Shuo Yang, Jiangxia Cao, Haipeng Li +2
In real-world applications, users always interact with items in multiple aspects, such as through implicit binary feedback (e.g., clicks, dislikes, long views) and explicit feedbac…
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation
Yu Liu, Hanbin Jiang, Lei Zhu +4
In the real world, users always have multiple interests while surfing different services to enrich their daily lives, e.g., watching hot short videos/live streamings. To describe u…
Cross-Domain Sequential Recommendation via Neural Process
Haipeng Li, Jiangxia Cao, Yiwen Gao +2
Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user interest modeling, which aims at utilizing a single model to predict the next items for differen…
Enhancing Content-based Recommendation via Large Language Model
Wentao Xu, Qianqian Xie, Shuo Yang +2
In real-world applications, users express different behaviors when they interact with different items, including implicit click/like interactions, and explicit comments/reviews int…