RecInterpreter aligns sequential recommender embeddings to LLaMA with a linear adapter and residual prompts on MovieLens100K and Steam.

Zhengyi Yang, Jiancan Wu, Yanchen Luo et al.

Survey on embedding techniques in recommender systems, covering matrix, sequential, and graph structures.

Maolin Wang, Xinjian Zhao, Wanyu Wang et al.

Proposes a Large Search Model (LSM) that unifies search tasks via prompt-driven LLMs, simplifying architecture and enhancing performance.

Liang Wang, Nan Yang, Xiaolong Huang et al.

AgentCF uses autonomous language agents for collaborative filtering, enhancing user behavior simulation in recommender systems.

Junjie Zhang, Yupeng Hou, Ruobing Xie et al.

Fine-tuned LLaMA as RepLLaMA and RankLLaMA, achieving 99.4% MRR on MS MARCO, significantly advancing multi-stage text retrieval.

Xueguang Ma, Liang Wang, Nan Yang et al.

RankVicuna, an open-source 7B model, achieves zero-shot listwise reranking comparable to GPT-3.5, with high reproducibility.

Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin

Proposes SAMRec, smoothing loss landscape to enhance Transformer-based sequential recommendation without self-supervised learning.

Vivian Lai, Huiyuan Chen, Chin-Chia Michael Yeh et al.

Proposes BIGRec, a two-step grounding framework combining fine-tuning and statistical info to enhance LLM recommendation, effective in few-shot and cross-domain settings.

Keqin Bao, Jizhi Zhang, Wenjie Wang et al.

Proposes GenQR and GenPRF frameworks using pretrained models to enhance retrieval effectiveness.

Xiao Wang, Sean MacAvaney, Craig Macdonald et al.

Proposed PACC and PACC-PE models significantly mitigate position bias in eCommerce search.

Yibo Wang, Yanbing Xue, Bo Liu et al.

This paper uncovers four widespread flaws in offline recommender system evaluation, demonstrating their impact with empirical evidence.

Balázs Hidasi, Ádám Tibor Czapp

Proposes C-3PO, a Yule-Simon-based probabilistic position bias model for short-video feeds, improving exposure estimation accuracy.

Olivier Jeunen

Formulates streaming CTR prediction, analyzes model performance under distribution shifts, improves via parameter tuning and exemplar replay.

Qi-Wei Wang, Hongyu Lu, Yu Chen et al.

MedCPT employs contrastive pre-training on 2.55B PubMed search logs, achieving state-of-the-art zero-shot biomedical IR performance surpassing larger models.

Qiao Jin, Won Kim, Qingyu Chen et al.

Introduced OBELICS, a web-scale open dataset of interleaved image-text documents, trained 80B-parameter IDEFICS, achieving state-of-the-art results.

Hugo Laurençon, Lucile Saulnier, Léo Tronchon et al.

Proposes a DIKW-based multimodal fusion framework using deep learning and feature selection, achieving 20% performance gain on public datasets.

Thanveer Shaik, Xiaohui Tao, Lin Li et al.

Proposes a hypernetwork-based controllable multi-objective re-ranking (CMR) framework enabling real-time preference adjustment without retraining, improving recommendation quality.

Sirui Chen, Yuan Wang, Zijing Wen et al.

This survey categorizes LLM-based recommendation into discriminative (e.g., BERT) and generative (e.g., GPT) paradigms, analyzing their techniques and performance.

Likang Wu, Zhi Zheng, Zhaopeng Qiu et al.

Proposes DAP, a post-hoc method to correct popularity bias in GCN recommenders, significantly improving tail item recommendations.

Jiajia Chen, Jiancan Wu, Jiawei Chen et al.

Proposes PMI-based importance masking in bottleneck MAE, boosting dense retrieval performance with 41.7% MRR@10 on MS MARCO.

Zehan Li, Yanzhao Zhang, Dingkun Long et al.