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Retaining Data from Streams of Social Platforms with Minimal Regret

Nguyen Thanh Tam, Matthias Weidlich, Duong Chi Thang, Hongzhi Yin, Nguyen Quoc Viet Hung
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
In this paper, we propose techniques to effectively decide which data to retain, such that the induced loss of information, the regret of neglecting certain data, is minimized.  ...  Today's social platforms, such as Twitter and Facebook, continuously generate massive volumes of data.  ...  Problem 1 (Retaining Problem with Minimal Regret).  ... 
doi:10.24963/ijcai.2017/397 dblp:conf/ijcai/TamWTYH17 fatcat:hbp3msub2zfjjektywma4svcpe

Social Big Data Analytics of Consumer Choices: A Two Sided Online Platform Perspective [article]

Meisam Hejazi Nia
2017 arXiv   pre-print
Using a large data set from eBay and empirical Bayesian estimation method, I quantify the bidders' anticipation of regret in various product categories, and investigate the role of experience in explaining  ...  This dissertation examines three distinct big data analytics problems related to the social aspects of consumers' choices.  ...  This further confirms that this platform does not have enough quality to retain its customers.  ... 
arXiv:1702.07074v1 fatcat:suq27p5v6ree5mdlzqzm5lvhpq

An Opportunistic Bandit Approach for User Interface Experimentation [article]

Nader Bouacida, Amit Pande, Xin Liu
2020 arXiv   pre-print
Facing growing competition from online rivals, the retail industry is increasingly investing in their online shopping platforms to win the high-stake battle of customer' loyalty.  ...  Through this paper, we demonstrate the effectiveness of opportunistic bandits to make the experiments as inexpensive as possible using real online retail data.  ...  For the sake of simplicity, we assume that the log stream is infinite. In practice, we simply cycle through the data log if we reach its end.  ... 
arXiv:2006.11873v1 fatcat:lnkhasu7anfg7dl7fsanoygyui

Commentaries on Relationship Marketing: The Present and Future of Customer Relationships in Services

Lena Steinhoff, Robert W. Palmatier, Kelly D. Martin, Grace Fox, Conor M. Henderson, Julian K. Saint Clair, Shuai Yan, Ju-Yeon Lee, Taylor Perko, Colleen M. Harmeling
2022 Journal of Service Management Research  
Four high-level strategies observed in current business practice bear the potential of fundamentally altering how service providers build and nurture relationships with their customers.  ...  Specifically, customer relationships in services have become more (1) data-based, (2) subscription-based, (3) sharing-based, and (4) experiences-based.  ...  From this, many virtual brand communities and video conferencing platforms emerged to allow people to continue to work from home and to connect with others at a safe distance.  ... 
doi:10.5771/2511-8676-2022-1-2 fatcat:zrcbfdmynfgntm6pilbwpoaioi

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

Fahim Ullah, Samad Sepasgozar, Changxin Wang
2018 Sustainability  
consumers' needs and minimizing their regrets.  ...  The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su10093142 fatcat:rftoc6botff4njulv3lxhfozty

On Predicting Deletions of Microblog Posts

Mossaab Bagdouri, Douglas W. Oard
2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15  
This paper addresses the problem of deletion prediction by analyzing the distribution of deleted tweets, presenting a new evaluation framework, exploring tweet-based and user-based features, and reporting  ...  Deletions occur for a variety of reasons, which can make the classification task challenging.  ...  ACKNOWLEDGMENT This work was made possible by NPRP grant# NPRP 6-1377-1-257 from the Qatar National Research Fund (a member of Qatar Foundation).  ... 
doi:10.1145/2806416.2806600 dblp:conf/cikm/BagdouriO15a fatcat:orhl6qmcpfcrtae4dj2lq77cuq

Detecting and Analyzing Privacy Leaks in Tweets

Paolo Cappellari, Soon Chun, Christopher Costello
2018 Proceedings of the 7th International Conference on Data Science, Technology and Applications  
Social network platforms are changing the way people interact not just with each other but also with companies and institutions.  ...  In this paper we propose an approach to assess the privacy content of the social posts with the goal of: protecting the users from inadvertently disclosing sensitive information, and rising awareness about  ...  Authors surveyed a number of users from both platforms to classify regretted posts into categories, and analyze the effort and time users spend in making amends for their posts, when possi-ble.  ... 
doi:10.5220/0006845602650275 dblp:conf/data/CappellariCC18 fatcat:de3ot57aprfchpb53rd6b6mn34

Perceptions of Retrospective Edits, Changes, and Deletion on Social Media

Günce Su Yilmaz, Fiona Gasaway, Blase Ur, Mainack Mondal
2021 International Conference on Web and Social Media  
Participants were aware retrospective modification impacts others, yet felt these impacts could be minimized through context-aware usage of markers and proactive notifications.  ...  We investigate perceptions of the necessity and acceptability of these mechanisms.  ...  They reported daily social media usage ranging from five minutes to over ten hours, with a median of one hour.  ... 
dblp:conf/icwsm/YilmazGUM21 fatcat:x6awvh5b5zb7je64o644vypcfa

Opinion Mining [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
Synonyms Instance language Definition The observation language used by a machine learning system is the language in which the observations it learns from are described.  ...  of activities on social platforms.  ...  processing the data as they arrive (in a single pass), since they cannot be retained permanently.  ... 
doi:10.1007/978-1-4899-7687-1_100511 fatcat:oluapsjgxzh6nlkqujjj562lzi

Beyond Social Media Analytics: Understanding Human Behaviour and Deep Emotion using Self Structuring Incremental Machine Learning [article]

Tharindu Bandaragoda
2020 arXiv   pre-print
Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data.  ...  This platform is demonstrated using two large datasets with over 1 million tweets.  ...  As application contributions, the above developed algorithms have been successfully trialled with social data streams from two online social media platforms.  ... 
arXiv:2009.09078v1 fatcat:izo3eyjc4vdo3jnttt7omvsv5q

Justice on the Digitized Field: Analyzing Online Responses to Technology-Facilitated Informal Justice through Social Network Analysis [chapter]

Ella Broadbent, Chrissy Thompson
2021 The Emerald International Handbook of Technology Facilitated Violence and Abuse  
Following two streams of findings, a model of social media user engagement was established that hierarchized the interplay between institutional and personal Twitter users.  ...  Employing Social Network Analysis to visualize the hierarchy of Twitter users responding to the incident and Applied Thematic Analysis to trace the diffusion of differing streams of sentiment within this  ...  with multiple different streams of sentiment.  ... 
doi:10.1108/978-1-83982-848-520211051 fatcat:7rq2rfcljrdr3oa6xuyfboogja

Survey on Fair Reinforcement Learning: Theory and Practice [article]

Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, Mykola Pechenizkiy
2022 arXiv   pre-print
Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques.  ...  We discuss various practical applications in which RL methods have been applied to achieve a fair solution with high accuracy.  ...  Consequently, counterfactual risk minimization techniques are leveraged to learn fair policies from biased offline data. In another work, Coston et al.  ... 
arXiv:2205.10032v1 fatcat:rrc7a5aumnbe3dmptkeh5ohapa

A survey of Big Data dimensions vs Social Networks analysis

Michele Ianni, Elio Masciari, Giancarlo Sperlí
2020 Journal of Intelligent Information Systems  
The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data.  ...  More in detail, the analysis of user generated data by popular social networks (i.e Facebook (, Twitter (, Instagram (, LinkedIn  ...  In the social media domain, a benchmarking of Big Data architecture using public cloud platforms has been discussed in Persico et al. (2018) for processing social streams.  ... 
doi:10.1007/s10844-020-00629-2 pmid:33191981 pmcid:PMC7649712 fatcat:3hvd5sshwzd67lxi4qlo2sgnwe

A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments [article]

Sindhu Padakandla
2020 arXiv   pre-print
The real-world complications of many tasks arising in these domains makes them difficult to solve with the basic assumptions underlying classical RL algorithms.  ...  This is possible either by minimizing the rewards lost during learning by RL agent or by finding a suitable policy for the RL agent which leads to efficient operation of the underlying system.  ...  With this information, the objective of the algorithm is to control the MDP in a manner such that regret is minimized.  ... 
arXiv:2005.10619v1 fatcat:35rikwhrwvcf7pvvn7rcokgzxq

To act or not to act? Academic acceleration worked in the past, so what's the current hold-up in New Zealand?

Janna Wardman
2015 Apex  
Despite a review of the literature showing the success of accelerative practices on academic and social outcomes, full-year acceleration is rarely implemented in New Zealand schools.  ...  This article begins by giving an understanding of the various forms of acceleration and a brief history of implementation.  ...  The Ministry of Education online learning site, Te Kete Ipurangi (TKI), has a section for the gifted and talented community.  ... 
doi:10.21307/apex-2015-010 fatcat:6p3vgdj6abd7xcigi4pqdxxwk4
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