Titlebook: Bias and Social Aspects in Search and Recommendation; First International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce

瘋狂

Rajashri Mahato,S. Saadhikha Shree,S. Ashair possible biases. This has led to a number of publications regarding algorithms for removing this bias from word embeddings. Debiasing should make the embeddings fairer in their use, avoiding potential negative effects downstream. For example: word embeddings with a gender bias that are used in a

myelography

可忽略

Saidmakhamadov Nosir,Karimov Bokhodirters are usually considered as two solutions of data-centric approach using the evaluation data to uncover the student abilities. Nevertheless, past lecturer recommendations can induced possible bias by using a single and immutable training set. We try to reduce this issue by releasing a hybrid reco

Decrepit

微塵

https://doi.org/10.1007/978-3-030-83122-6g a book. Their exploration can greatly benefit end-users in their daily life. As data consumers are being empowered, there is a need for a tool to express end-to-end data pipelines for the personalized exploration of rated datasets. Such a tool must be easy to use as several strategies need to be t

灰姑娘

Noisome

口訣

CHURL

Predicting 30-Day Emergency Readmission Risks’ features with the users’ preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted f

組成

Predicting 30-Day Emergency Readmission Risk been studied on users’ behavior. There has been recent work that have focused on how online social network behavior and activity can impact users’ offline behavior. In this paper, we study the inverse where we focus on whether users’ offline behavior captured through their check-ins at different ve