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Found 6 papers, 3 papers with code
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.
Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence
To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item's exposure to the user's neighbors; and (2) adjusting the exposure of a target item to target users' neighbors to trade off steering performance and the damage to the neighbors' experience.
Incorporate LLMs with Influential Recommender System
Recommender systems have achieved increasing accuracy over the years.
Proactive Recommendation with Iterative Preference Guidance
However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization.
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning.
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings.
