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Found 13 papers, 4 papers with code
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains.
Privacy-Preserving Sequential Recommendation with Collaborative Confusion
Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence.
TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs
We construct a hierarchical task tree encompassing 7 major areas covering over 200 categories and over 800 tasks, which covers diverse capabilities such as question answering, reasoning, multiturn dialogue, and text generation, to evaluate LLMs in a comprehensive and in-depth manner.
Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains
This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features.
Adaptation Speed Analysis for Fairness-aware Causal Models
In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable.
Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data
This ensures that Fedward can maintain the performance for the Non-IID scenario.
A Self-Correcting Sequential Recommender
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
Modeling Sequential Recommendation as Missing Information Imputation
To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems
Most existing methods can be categorized as \emph{multi-view representation fusion}; they first build one graph and then integrate multi-view data into a single compact representation for each node in the graph.
Meta Matrix Factorization for Federated Rating Predictions
Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module.
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations
We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i. e., they have a shared account) and in which user behavior is available in multiple domains (i. e., recommendations are cross-domain).
Improving Outfit Recommendation with Co-supervision of Fashion Generation
FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information.
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation
The generated comments can be regarded as explanations for the recommendation results.
