4 citations · 4 across the 8 of their papers we have counts for
9 papers
UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation
Jun Yang, Jiasi Chen, Jiawei Sun +4
Semantic-ID-based generative recommendation unifies retrieval and ranking through autoregressive decoding but differs structurally from discriminative models, which can directly ac…
EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search
Shuwei Yuan, Mingqian Ding, Luxin Liu +2
E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulati…
SAM-D2Q: Aligning Multimodal Doc2Query with Search Demand and Conversion for E-commerce
Hui Zhou, Jian Hui Ji, Lei Ma +2
E-commerce search often suffers from vocabulary mismatch between user queries and merchant-authored product titles, since short titles cannot fully cover diverse user expressions o…
UniSGR: Unified Framework for Semantic ID Generation and Ranking
Jiawei Sun, Jun Yang, Ziyue Guo +4
Recommendation systems play a pivotal role in modern e-commerce platforms. While generative retrieval has emerged as a promising paradigm for alleviating the limitations of multi-s…
Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation
Yantao Yu, Sen Qiao, Lei Shen +2
Recent progress in scaling large models has motivated recommender systems to increase model depth and capacity to better leverage massive behavioral data. However, recommendation i…
GateSID: Adaptive Gating for Balancing Semantic and Collaborative Signals in Recommendation
Hai Zhu, Yantao Yu, Lei Shen +2
In cold-start scenarios, the scarcity of collaborative signals for new items exacerbates the Matthew effect, undermining platform diversity and posing a persistent challenge in pra…
SORT: A Systematically Optimized Ranking Transformer for Industrial-scale Recommenders
Chunqi Wang, Bingchao Wu, Taotian Pang +9
While Transformers have achieved remarkable success in LLMs through superior scalability, their application in industrial-scale ranking models remains nascent, hindered by the chal…
SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress
Yang Yu, Lei Kou, Huaikuan Yi +6
With the rapid evolution of Large Language Models (LLMs), generative recommendation is gradually reshaping the paradigm of recommender systems. However, most existing methods remai…
Scaling Transformers for Discriminative Recommendation via Generative Pretraining
Chunqi Wang, Bingchao Wu, Zheng Chen +3
Discriminative recommendation tasks, such as CTR (click-through rate) and CVR (conversion rate) prediction, play critical roles in the ranking stage of large-scale industrial recom…