標題: Titlebook: Bias and Social Aspects in Search and Recommendation; First International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce [打印本頁] 作者: culinary 時間: 2025-3-21 16:20
書目名稱Bias and Social Aspects in Search and Recommendation影響因子(影響力)
書目名稱Bias and Social Aspects in Search and Recommendation影響因子(影響力)學科排名
書目名稱Bias and Social Aspects in Search and Recommendation網(wǎng)絡公開度
書目名稱Bias and Social Aspects in Search and Recommendation網(wǎng)絡公開度學科排名
書目名稱Bias and Social Aspects in Search and Recommendation被引頻次
書目名稱Bias and Social Aspects in Search and Recommendation被引頻次學科排名
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書目名稱Bias and Social Aspects in Search and Recommendation年度引用學科排名
書目名稱Bias and Social Aspects in Search and Recommendation讀者反饋
書目名稱Bias and Social Aspects in Search and Recommendation讀者反饋學科排名
作者: 謙虛的人 時間: 2025-3-21 22:35
Mitigating Gender Bias in Machine Learning Data Sets,rithms has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine le作者: A保存的 時間: 2025-3-22 03:38 作者: 思鄉(xiāng)病 時間: 2025-3-22 05:47 作者: 征服 時間: 2025-3-22 10:10 作者: 顛簸下上 時間: 2025-3-22 12:53 作者: 易怒 時間: 2025-3-22 17:56
: Exploring the Bias of Web Domains Through the Eyes of Users,em, that based on the web graph computes the bias characteristics of web domains to user-defined concepts. Our approach uses adaptations of propagation models and a variation of the pagerank algorithm named ., that models various behaviours of biased surfers. Currently, the system runs over a subset作者: 租約 時間: 2025-3-23 01:12 作者: Neuralgia 時間: 2025-3-23 02:19 作者: 柳樹；枯黃 時間: 2025-3-23 07:53
A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity,wever, a unique user profile is created, containing all our preferences. Suppose that a company wants to understand who are its customers. It wants to treat costumers as a target, and understand what campaigns the company should run on them. On the one hand, an approach that clusters the users and p作者: 生意行為 時間: 2025-3-23 12:21
What Kind of Content Are You Prone to Tweet? Multi-topic Preference Model for Tweeters,t of interest in a certain topic is a challenging task, especially considering the massive digital information they are exposed to. For example, in the context of Twitter, aligned with his/her preferences a user may tweet and retweet more about technology than sports and do not share any music-relat作者: 大笑 時間: 2025-3-23 15:16 作者: 毗鄰 時間: 2025-3-23 22:04
,The Impact of Foursquare Checkins on Users’ Emotions on Twitter, 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作者: 退出可食用 時間: 2025-3-24 01:51
Improving News Personalization Through Search Logs,est profiles that are matched with news articles in order to properly decide which articles are to be recommended. When constructing user profiles, existing personalization methods exploit the user activity observed within the news service itself without incorporating information from other sources.作者: helper-T-cells 時間: 2025-3-24 04:24
Analyzing the Interaction of Users with News Articles to Create Personalization Services,by taking advantage of individual preferences, content of news portals can be tailored on the bases of sociological aspects (e.g., the demographics of the users or the region in which they live) elicited from user interactions with the news. This allows to generate personalization with a coarse gran作者: moribund 時間: 2025-3-24 10:29
Using String-Comparison Measures to Improve and Evaluate Collaborative Filtering Recommender System in this type of system is the Collaborative Filtering which recommends products to users based on their interactions and on what items similar users have liked in the past. However, many traditional methods for determining similarity do not consider temporal information neither the rich information作者: laxative 時間: 2025-3-24 12:32
Enriching Product Catalogs with User Opinions,nowledge for both manufacturers and customers. However, reviews often bring so much information that exceeds the human capacity of reasoning and hampers their effective use. Thus, researchers on how to organize a large number of opinions available on the reviews in the Web play a substantial role. T作者: Pituitary-Gland 時間: 2025-3-24 15:47
1865-0929 held in April, 2020. Due to the COVID-19 pandemic BIAS 2020 was held virtually.?.The 10 full papers and 7 short papers were carefully reviewed and seleced from 44 submissions.?The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impac作者: ODIUM 時間: 2025-3-24 19:13 作者: 鞭打 時間: 2025-3-25 01:01 作者: 熔巖 時間: 2025-3-25 03:57 作者: Basal-Ganglia 時間: 2025-3-25 09:35 作者: Euthyroid 時間: 2025-3-25 15:20 作者: 線 時間: 2025-3-25 19:35
Venue Suggestion Using Social-Centric Scores,rom venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues’ content.作者: 做作 時間: 2025-3-25 21:42
,The Impact of Foursquare Checkins on Users’ Emotions on Twitter,nues on Foursquare can impact users’ online emotion expression as depicted in their tweets. We show that users’ offline activity can impact users’ online emotions; however, the type of activity determines the extent to which a user’s emotions will be impacted.作者: maculated 時間: 2025-3-26 02:12 作者: 好色 時間: 2025-3-26 06:08 作者: modest 時間: 2025-3-26 09:22
Ming Yang,Zhizong Wu,Zhiguo Yanrelevance, diversity, and related concepts. Then, it focuses on explaining the emerging concept of fairness in various recommendation settings. In doing so, this paper presents comparisons and highlights contracts among various measures, and gaps in our conceptual and evaluative frameworks.作者: 艱苦地移動 時間: 2025-3-26 14:52 作者: 肌肉 時間: 2025-3-26 18:15
https://doi.org/10.1007/978-981-19-8136-4nking the recommendations under preferential fairness constraints. Our experimental results demonstrate that the state of fairness can be reached with minimal accuracy compromises for both binary and non-binary attributes.作者: cavity 時間: 2025-3-26 21:10 作者: Condescending 時間: 2025-3-27 03:24 作者: 消滅 時間: 2025-3-27 05:36 作者: Credence 時間: 2025-3-27 13:26 作者: 正式演說 時間: 2025-3-27 16:26
Zhiwei Tang,Lingling Hu,Jianye Lia for machine learning. The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact in the context of search and recommender systems.作者: corpuscle 時間: 2025-3-27 18:48 作者: 意外的成功 時間: 2025-3-28 00:19 作者: orthodox 時間: 2025-3-28 03:54 作者: Blazon 時間: 2025-3-28 10:19 作者: heirloom 時間: 2025-3-28 11:47 作者: 紡織品 時間: 2025-3-28 16:42
Analyzing the Interaction of Users with News Articles to Create Personalization Services,sent in the corpus spans for a range of 4 months, and was extracted from real user interactions with news items in the Yahoo Web portal. The dataset has been analyzed in order to understand users behaviors and their relations with sociological aspects. Thanks to our analysis, different forms of personalization can be generated.作者: 遭遇 時間: 2025-3-28 18:53
Enriching Product Catalogs with User Opinions,uld guide the process of organizing opinions. This paper presents a summary of an approach called ., based on machine-learning techniques, to enrich a product catalog with opinions extracted from product reviews. The experimental results demonstrate the effectiveness of the proposed approach.作者: defibrillator 時間: 2025-3-29 01:51
Saidmakhamadov Nosir,Karimov Bokhodirters selecting activities and building an evaluation path based on historical evolutions of past students. In this paper, we particularly highlight the crucial clustering task by offering plots and metrics to adjust the decisions of the practitioners.作者: NEG 時間: 2025-3-29 06:26
,Recommendation Filtering à la carte for Intelligent Tutoring Systems,ters selecting activities and building an evaluation path based on historical evolutions of past students. In this paper, we particularly highlight the crucial clustering task by offering plots and metrics to adjust the decisions of the practitioners.作者: Glucocorticoids 時間: 2025-3-29 09:51 作者: enchant 時間: 2025-3-29 12:51 作者: 令人作嘔 時間: 2025-3-29 18:09
978-3-030-52484-5Springer Nature Switzerland AG 2020作者: 狂亂 時間: 2025-3-29 23:08
Ming Yang,Zhizong Wu,Zhiguo Yan a certain amount of fairness in search is crucial to not only creating a more balanced environment that considers relevance and diversity but also providing a more sustainable way forward for both content consumers and content producers. This short paper examines some of the recent works to define 作者: Apoptosis 時間: 2025-3-30 01:42 作者: 廚師 時間: 2025-3-30 05:13
An Approach to Mine Low-Frequency Item-Setsy exists could so far only be tested to a very limited extent. Yet to guarantee the accountability of recommendation and information filtering systems, society needs to be able to determine whether they comply with ethical and legal requirements. This paper focuses on black box analyses as methods t作者: 瘋狂 時間: 2025-3-30 08:55
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 作者: myelography 時間: 2025-3-30 12:35 作者: 可忽略 時間: 2025-3-30 18:37
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作者: Decrepit 時間: 2025-3-30 21:43 作者: 微塵 時間: 2025-3-31 02:46
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作者: 灰姑娘 時間: 2025-3-31 08:12 作者: Noisome 時間: 2025-3-31 09:35 作者: 口訣 時間: 2025-3-31 13:27 作者: CHURL 時間: 2025-3-31 20:10
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作者: 組成 時間: 2025-3-31 21:54
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作者: 外來 時間: 2025-4-1 01:58
Predicting 30-Day Emergency Readmission Riskest profiles that are matched with news articles in order to properly decide which articles are to be recommended. When constructing user profiles, existing personalization methods exploit the user activity observed within the news service itself without incorporating information from other sources.作者: ostracize 時間: 2025-4-1 06:18 作者: Oafishness 時間: 2025-4-1 13:30
Springer Series in Optical Sciences in this type of system is the Collaborative Filtering which recommends products to users based on their interactions and on what items similar users have liked in the past. However, many traditional methods for determining similarity do not consider temporal information neither the rich information
