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Neural re-ranking in multi-stage recommender systems: A review
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 …
user experience and satisfaction by rearranging the input ranking lists, and thereby plays a …
[PDF][PDF] Llm-enhanced reranking in recommender systems
Reranking is a critical component in recommender systems, playing an essential role in
refining the output of recommendation algorithms. Traditional reranking models have …
refining the output of recommendation algorithms. Traditional reranking models have …
A survey on intent-aware recommender systems
Many modern online services feature personalized recommendations. A central challenge
when providing such recommendations is that the reason why an individual user accesses …
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
Online reviews play a crucial role in comprehending user rating behavior and improving
personalized recommendations in e-commerce. However, existing review-based …
personalized recommendations in e-commerce. However, existing review-based …
Multimodal representation learning for tourism recommendation with two-tower architecture
Personalized recommendation plays an important role in many online service fields. In the
field of tourism recommendation, tourist attractions contain rich context and content …
field of tourism recommendation, tourist attractions contain rich context and content …
Recommendation of mix-and-match clothing by modeling indirect personal compatibility
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 …
increasing research interest. It is a challenging task because it often needs to use …
Utility-Oriented Reranking with Counterfactual Context
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' …
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 …
supervision signals and redundant entity relations, which can diminish the advantages of …
Multi-channel Integrated Recommendation with Exposure Constraints
Integrated recommendation, which aims at jointly recommending heterogeneous items from
different channels in a main feed, has been widely applied to various online platforms …
different channels in a main feed, has been widely applied to various online platforms …
Personalized diversification for neural re-ranking in recommendation
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 …
modeling the listwise context and the cross-item interactions between the candidate items …