49 citations · 71 across the 35 of their papers we have counts for
8 papers · 1 filter
Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
Xiaopeng Li, Jingtong Gao, Pengyue Jia +7
Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention. Ho…
Large Language Model Enhanced Recommender Systems: A Survey
Qidong Liu, Xiangyu Zhao, Yuhao Wang +9
Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the…
GPRec: Bi-level User Modeling for Deep Recommenders
Yejing Wang, Dong Xu, Xiangyu Zhao +7
GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a divers…
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
Qidong Liu, Xian Wu, Wanyu Wang +5
Sequential Recommender Systems (SRS), which model a user's interaction history to predict the next item of interest, are widely used in various applications. However, existing SRS…
SIGMA: Selective Gated Mamba for Sequential Recommendation
Ziwei Liu, Qidong Liu, Yejing Wang +6
In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences. Typically, SRS utilize transf…
Prompt Tuning as User Inherent Profile Inference Machine
Yusheng Lu, Zhaocheng Du, Xiangyang Li +9
Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capab…
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
Qidong Liu, Xian Wu, Yejing Wang +4
Sequential recommender systems (SRS) aim to predict users' subsequent choices based on their historical interactions and have found applications in diverse fields such as e-commerc…
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
Pengyue Jia, Yejing Wang, Zhaocheng Du +6
Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently…