Neural re-ranking in multi-stage recommender systems: A review

W Liu, Y **, J Qin, F Sun, B Chen, W Zhang… - arxiv preprint arxiv …, 2022‏ - arxiv.org
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects
user experience and satisfaction by rearranging the input ranking lists, and thereby plays a …

[PDF][PDF] Llm-enhanced reranking in recommender systems

J Gao, B Chen, X Zhao, W Liu, X Li… - arxiv preprint arxiv …, 2024‏ - researchgate.net
Reranking is a critical component in recommender systems, playing an essential role in
refining the output of recommendation algorithms. Traditional reranking models have …

A survey on intent-aware recommender systems

D Jannach, M Zanker - ACM Transactions on Recommender Systems, 2024‏ - dl.acm.org
Many modern online services feature personalized recommendations. A central challenge
when providing such recommendations is that the reason why an individual user accesses …

Cognitive process-driven model design: A deep learning recommendation model with textual review and context

L Wang, X Zhao, N Liu, Z Shen, C Zou - Decision Support Systems, 2024‏ - Elsevier
Online reviews play a crucial role in comprehending user rating behavior and improving
personalized recommendations in e-commerce. However, existing review-based …

Multimodal representation learning for tourism recommendation with two-tower architecture

Y Cui, S Liang, YY Zhang - Plos one, 2024‏ - journals.plos.org
Personalized recommendation plays an important role in many online service fields. In the
field of tourism recommendation, tourist attractions contain rich context and content …

Recommendation of mix-and-match clothing by modeling indirect personal compatibility

S Liao, Y Ding, PY Mok - Proceedings of the 2023 ACM International …, 2023‏ - dl.acm.org
Fashion recommendation considers both product similarity and compatibility, and has drawn
increasing research interest. It is a challenging task because it often needs to use …

Utility-Oriented Reranking with Counterfactual Context

Y **, W Liu, X Dai, R Tang, Q Liu, W Zhang… - ACM Transactions on …, 2024‏ - dl.acm.org
As a critical task for large-scale commercial recommender systems, reranking rearranges
items in the initial ranking lists from the previous ranking stage to better meet users' …

Dual intent view contrastive learning for knowledge aware recommender systems

J Guo, Z Yin, S Feng, D Yao, S Liu - Scientific Reports, 2025‏ - nature.com
Abstract Knowledge-aware recommendation systems often face challenges owing to sparse
supervision signals and redundant entity relations, which can diminish the advantages of …

Multi-channel Integrated Recommendation with Exposure Constraints

Y Xu, Q Shen, J Yin, Z Deng, D Wang, H Chen… - Proceedings of the 29th …, 2023‏ - dl.acm.org
Integrated recommendation, which aims at jointly recommending heterogeneous items from
different channels in a main feed, has been widely applied to various online platforms …

Personalized diversification for neural re-ranking in recommendation

W Liu, Y **, J Qin, X Dai, R Tang, S Li… - 2023 IEEE 39th …, 2023‏ - ieeexplore.ieee.org
Re-ranking, as the final stage of the multi-stage recommender systems (MRS), aims at
modeling the listwise context and the cross-item interactions between the candidate items …