The study evaluates large language models (LLMs) as rerankers in conversational movie recommendation, comparing proprietary, open-weight, and fine-tuned LLMs against collaborative-filtering and sequential baselines on the ReDial benchmark. Results show that the best proprietary LLM achieves an NDCG@10 of 0.1497 with a shared semantic candidate pool, outperforming non-LLM baselines, while open-weight LLMs do not surpass a tuned shallow autoencoder under the same protocol. The analysis also highlights that reranker performance is highly sensitive to candidate generation, pool size, scoring policy, and decoding temperature, suggesting these factors should be reported as standard evaluation fields.
By Ante Kapetanovic, Tomislav Duricic, Andro Mercep, Emanuel Lacic
arXiv:2607. 25420v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pre-trained backbones alone when they are placed inside a structured recommendation pipeline.
By Jiahao Tian, Zhenkai Wang
LIGE‑GR is a framework that transitions traditional ranking‑based recommender systems to a generative, listwise approach inspired by large language models. It extends existing pointwise recommendation models into a listwise generation system, enabling sequence‑level optimization without overhauling the entire infrastructure. Experiments on Instagram Reels and Facebook Video show modest gains in user time spent—1.14 % and 0.72 % respectively—while adding only slight inference overhead.
By Venkat Srinivas, Chenzhang He, Sam Woodmansee, Shawn Lian, Wenjie Hu, Renjie Jiang, Ziheng Huang, Xinyuan Zhang, Zhihao Zheng, Zhuoran Yu, Rui Li, Lei Yuan, Ziwei Li, Jimmy Jia, Mert Terzihan, Ekrem Kocaguneli, Yiming Liao, Zhichen Zhao, Yue Yin, Yue Weng, Wanlin Ma, Xufeng Cai, Weimiao Wu, Yezhou Huang, Du Zhang, Yukun Ding, Aaron Johnston, Yueming Wang, Zhaojie Gong, Yuting Zhang, Serena Li, Adithya Ganesh, Boying Liu, Haichuan Yang, Xialu Li, Matt Ma, Qunshu Zhang, John Joshua Miller, Praveen Rathinavelu, Cheng Huang, Aadhar Sachdeva, Josh Karns, Andres Aaron Gutierrez, Neil Agarwal, Gustas Pladis, Vladimir Batygin, Gopal Ray, Aditya Priyadarshi, Shantanu Patil, Zhe Wang, Penny Pan, Yiping Han, Arun Singh, Guangdeng Liao, Bi Xue, Xinyao Hu, Yang Song, Yisong Song, Meihong Wang, Haotian Wu, Deepak Agarwal, Ji Liu
The paper examines LLM-based recommendation rerankers that are often evaluated under an oracle protocol, which guarantees the ground-truth item is present in the scored set. Across Amazon datasets, this protocol overestimates realistic NDCG@10 by 92–95% because realistic retrieval only covers 2–19% of relevant items at K=100, creating a recall ceiling that limits any closed-candidate reranker's top‑k NDCG. The authors find that various optimisation strategies—including prompt engineering, model scaling, sequential models, supervised neural rerankers, LoRA fine‑tuning, hybrid retrieval, score‑aware prompting, and LLM+CF fusion—do not significantly improve over a collaborative‑filtering baseline under realistic retrieval, and they propose a Recall‑Aware Evaluation Protocol (RAEP) to better assess rerankers in low‑recall regimes.
By Zhaohui Wang
Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pre-trained backbones alone when they are placed inside a structured recommendation pipeline. In this paper, we present MARS, a modular multi-agent re-ranking framework for repeat-order food delivery recommendation.
arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.
By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai
arXiv:2608.30333v1 Announce Type: cross
Abstract: Next-basket repurchase recommendation is commonly formulated as a ranking task: given a customer's purchase history, the system ranks previously purc...
By Yanan Cao, Anay Dombe, Murali Mohana Krishna Dandu, Shreeranjani Srirangamsridharan, Sinduja Subramaniam, Yogananth Mahalingam, Evren Korpeoglu, Kannan Achan
ZooWork-ShopRanker is a family of open e‑commerce rerankers (0.6B, 4B, and 8B) that align with human shopping preferences by using large language models as preference oracles to generate training pairs. The flagship 8B model serves as a teacher for the smaller 4B and 0.6B models, which are further refined on judged pairs. A new benchmark, ShopRank‑Bench, contains ~10,000 private‑traffic preference pairs and shows that all ZooWork models outperform the strongest open reranker baseline and their own un‑aligned versions.
By Siqiao Xue, Shuxuan Liu, Ning Hu
This reproducibility study confirms that incorporating generated natural‑language user profiles into recommender systems enhances transparency and allows users to directly intervene by correcting preferences or addressing cold‑start issues. The authors replicated the original findings and extended the evaluation with context ablation, multi‑seed stability tests, and mechanistic interpretability analysis using nnsight. Their results show that while perturbing profiles shifts predicted ratings uniformly across genres, the overall rankings remain unchanged, attributing this to the rating‑regression objective rather than the profile interface.
By Noah Mami\'e, Laurin van den Bergh
arXiv:2607. 27577v1 Announce Type: cross Abstract: Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.
By Di Bai, Jintao Liu, Zhenwei Tang, Peifan Wu, Nada Al-Thawr, Luoshu Wang
arXiv:2602.18613v2 Announce Type: replace
Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with r...
By Baris Arat, Emre Sefer
The paper introduces SARA, an industrial framework that scales articulated user rationales (AURs) for recommendation systems. It curates a high‑quality AUR dataset from 240 M users, trains a 7B‑parameter MLLM (SARA‑7B) to generate rationales for millions of authors, and integrates these generated rationales into a production ranking model (SARA‑Ranker). Offline and online experiments demonstrate that the system produces more specific, polarity‑consistent rationales and improves user engagement while reducing negative feedback.
By Haoke Xiao, Yueyang Liu, Yuhui Zhang, Xiang Chen, Yufei Liu, Jia Xu, Yalong Guan, Xiaolan Zhu, Xiaoyu Zhang, Shijun Wang, Shuang Yang, Zijie Meng, Zejian Zhang, Ruochen Yang, Xiangyu Wu, Tingting Gao, Han Li, Lantao Hu, Cheng Luo, Kun Gai