Tootfinder

Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain
Pratik Gajane, Sean Newman, John D. Piette
https://arxiv.org/abs/2402.19226

Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain
This study investigates gender fairness in personalized pain care recommendations using machine learning algorithms. Leveraging a contextual bandits framework, personalized recommendations are formulated and evaluated using LinUCB algorithm on a dataset comprising interactions with $164$ patients across $10$ sessions each. Results indicate that while adjustments to algorithm parameters influence the quality of pain care recommendations, this impact remains consistent across genders. However, wh…

AI companies to universities: Personalized tutors will make you obsolete
Also AI companies: Thanks for recording your lectures so we can sell them on the open market to train personalized tutors
https://annettevee.substack.com/p/when-student-data-is-the-new-oil

RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
Yang Xu, Yunlin Tan, Cheng Zhang, Kai Chi, Peng Sun, Wenyuan Yang, Ju Ren, Hongbo Jiang, Yaoxue Zhang
https://arxiv.org/abs/2402.19054

RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
Embedding watermarks into models has been widely used to protect model ownership in federated learning (FL). However, existing methods are inadequate for protecting the ownership of personalized models acquired by clients in personalized FL (PFL). This is due to the aggregation of the global model in PFL, resulting in conflicts over clients' private watermarks. Moreover, malicious clients may tamper with embedded watermarks to facilitate model leakage and evade accountability. This paper presen…

👁️ EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning
(... reminds of that movie Looker)
#ai

EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning
From a visual perception perspective, modern graphical user interfaces (GUIs) comprise a complex graphics-rich two-dimensional visuospatial arrangement of text, images, and interactive objects such as buttons and menus. While existing models can accurately predict regions and objects that are likely to attract attention ``on average'', so far there is no scanpath model capable of predicting scanpaths for an individual. To close this gap, we introduce EyeFormer, which leverages a Transformer arc…

[RE] Modeling Personalized Item Frequency Information for Next-basket Recommendation
S{\l}awomir Garcarz, Avik Pal, Pim Praat
https://arxiv.org/abs/2402.17925

[RE] Modeling Personalized Item Frequency Information for Next-basket Recommendation
This paper focuses on reproducing and extending the results of the paper: "Modeling Personalized Item Frequency Information for Next-basket Recommendation" which introduced the TIFU-KNN model and proposed to utilize Personalized Item Frequency (PIF) for Next Basket Recommendation (NBR). We utilized publicly available grocery shopping datasets used in the original paper and incorporated additional datasets to assess the generalizability of the findings. We evaluated the performance of the models…

This https://arxiv.org/abs/2404.14563 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults
This study explores the possibility of facilitating algorithmic decision-making by combining interpretable artificial intelligence (XAI) techniques with sensor data, with the aim of providing researchers and clinicians with personalized analyses of cannabis intoxication behavior. SHAP analyzes the importance and quantifies the impact of specific factors such as environmental noise or heart rate, enabling clinicians to pinpoint influential behaviors and environmental conditions. SkopeRules simpl…

HyperFedNet: Communication-Efficient Personalized Federated Learning Via Hypernetwork
Xingyun Chen, Yan Huang, Zhenzhen Xie, Junjie Pang
https://arxiv.org/abs/2402.18445

HyperFedNet: Communication-Efficient Personalized Federated Learning Via Hypernetwork
There are still many challenges in Federated Learning (FL). First, during the model update process, the model parameters on the local user need to be sent to the server for aggregation. This involves the consumption of network bandwidth, especially when the number of users participating in FL is large. High communication costs may limit the application of FL in certain scenarios. Secondly, since users participating in FL usually have different data distributions, this heterogeneity of data may …

This https://arxiv.org/abs/2402.17279 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

DiFashion: Towards Personalized Outfit Generation
The evolution of Outfit Recommendation (OR) in the realm of fashion has progressed through two distinct phases: Pre-defined Outfit Recommendation and Personalized Outfit Composition. Despite these advancements, both phases face limitations imposed by existing fashion products, hindering their effectiveness in meeting users' diverse fashion needs. The emergence of AI-generated content has paved the way for OR to overcome these constraints, demonstrating the potential for personalized outfit gene…

Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Jizhi Zhang, Keqin Bao, Wenjie Wang, Yang Zhang, Wentao Shi, Wanhong Xu, Fuli Feng, Tat-Seng Chua
https://arxiv.org/abs/2402.18240

Prospect Personalized Recommendation on Large Language Model-based Agent Platform
The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Agent platforms and introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agent…

DiFashion: Towards Personalized Outfit Generation
Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He
https://arxiv.org/abs/2402.17279 h…

DiFashion: Towards Personalized Outfit Generation
The evolution of Outfit Recommendation (OR) in the realm of fashion has progressed through two distinct phases: Pre-defined Outfit Recommendation and Personalized Outfit Composition. Despite these advancements, both phases face limitations imposed by existing fashion products, hindering their effectiveness in meeting users' diverse fashion needs. The emergence of AI-generated content has paved the way for OR to overcome these constraints, demonstrating the potential for personalized outfit gene…