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This is a presentation at the Workshop on Future TV, at the EuroITV2010 Conference, Tampere, Finland
EuroITV2010: Linking TV and (Social) Web: NoTube Use Case
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This is a joint minor program by VU University Amsterdam & University of Amsterdam. It is now open for registration. For more information please visit the official website: http://www.centrefordigitalhumanities.nl/minor-digital-humanities/
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This is a presentation at the Workshop on Future TV, at the EuroITV2010 Conference, Tampere, Finland
EuroITV2010: Linking TV and (Social) Web: NoTube Use Case
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This is a joint minor program by VU University Amsterdam & University of Amsterdam. It is now open for registration. For more information please visit the official website: http://www.centrefordigitalhumanities.nl/minor-digital-humanities/
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Social Recommender Systems Tutorial - WWW 2011
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In this paper, we propose user modeling strategies which use Concept Frequency - Inverse Document Frequency (CF- IDF) as a weighting scheme and incorporate either or both of the dynamics and semantics of user interests. To this end, we first provide a comparative study on different user modeling strategies considering the dynamics of user interests in previous literature to present their comparative performance. In addition, we investigate different types of information (i.e., categories, classes and connected entities via various proper- ties) for entities from DBpedia and the combination of them for extending user interest profiles. Finally, we build our user modeling strategies incorporating either or both of the best- performing methods in each dimension. Results show that our strategies outperform two baseline strategies significantly in the context of link recommendations on Twitter.
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To utilize Large Language Models (LLMs) for content-based recommendation systems in entertainment platforms, follow these steps: Data Collection: Gather diverse datasets of entertainment content with metadata. Preprocessing: Clean, tokenize, and encode textual data for model input. Model Selection: Choose an LLM architecture like GPT-3 and fine-tune it on the dataset. Feature Extraction: Extract relevant features from the data, such as genre, keywords, and sentiment. Recommendation Generation: Utilize the fine-tuned LLM to generate personalized recommendations based on user preferences and content features. Evaluation and Optimization: Assess recommendation quality and iterate for continual improvement. https://www.leewayhertz.com/build-content-based-recommendation-for-entertainment-using-llms/
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In this paper, we propose user modeling strategies which use Concept Frequency - Inverse Document Frequency (CF- IDF) as a weighting scheme and incorporate either or both of the dynamics and semantics of user interests. To this end, we first provide a comparative study on different user modeling strategies considering the dynamics of user interests in previous literature to present their comparative performance. In addition, we investigate different types of information (i.e., categories, classes and connected entities via various proper- ties) for entities from DBpedia and the combination of them for extending user interest profiles. Finally, we build our user modeling strategies incorporating either or both of the best- performing methods in each dimension. Results show that our strategies outperform two baseline strategies significantly in the context of link recommendations on Twitter.
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Social recommender system
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A presentation of the Corrib clan that was shown in Korea
Slawek Korea
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To utilize Large Language Models (LLMs) for content-based recommendation systems in entertainment platforms, follow these steps: Data Collection: Gather diverse datasets of entertainment content with metadata. Preprocessing: Clean, tokenize, and encode textual data for model input. Model Selection: Choose an LLM architecture like GPT-3 and fine-tune it on the dataset. Feature Extraction: Extract relevant features from the data, such as genre, keywords, and sentiment. Recommendation Generation: Utilize the fine-tuned LLM to generate personalized recommendations based on user preferences and content features. Evaluation and Optimization: Assess recommendation quality and iterate for continual improvement. https://www.leewayhertz.com/build-content-based-recommendation-for-entertainment-using-llms/
How to use LLMs for creating a content-based recommendation system for entert...
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CATS4ML Data Challenge: Crowdsourcing Adverse Test Sets for Machine Learning
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Presentation at the ACM-W
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Harnessing Human Semantics at Scale (updated)
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Keynote at ODSC2020 Conference
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Data excellence: Better data for better AI
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Semantics-driven Recommendations and Personalized Museum Tour Generation
CHIP Demonstrator presentation @ CATCH Symposium
CHIP Demonstrator presentation @ CATCH Symposium
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Presentation at the Semantic Web Challenge of the CHIP demonstrator: Semantics-driven Recommendations and Personalized Museum Tour Generation
Semantic Web Challenge: CHIP Demonstrator
Semantic Web Challenge: CHIP Demonstrator
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Presentation at ISWC2018: http://iswc2018.semanticweb.org/sessions/the-rijksmuseum-collection-as-linked-data/ of our paper published originally in the Semantic Web Journal: http://www.semantic-web-journal.net/content/rijksmuseum-collection-linked-data-2 Many museums are currently providing online access to their collections. The state of the art research in the last decade shows that it is beneficial for institutions to provide their datasets as Linked Data in order to achieve easy cross-referencing, interlinking and integration. In this paper, we present the Rijksmuseum linked dataset (accessible at http://datahub.io/dataset/rijksmuseum), along with collection and vocabulary statistics, as well as lessons learned from the process of converting the collection to Linked Data. The version of March 2016 contains over 350,000 objects, including detailed descriptions and high-quality images released under a public domain license.
The Rijksmuseum Collection as Linked Data
The Rijksmuseum Collection as Linked Data
Lora Aroyo
My keynote at the ICAL2018 Conference @ Rijksmuseum
Keynote at International Conference of Art Libraries 2018 @Rijksmuseum
Keynote at International Conference of Art Libraries 2018 @Rijksmuseum
Lora Aroyo
Presentation at the NYC Media Lab (NYCML2018). There is a growing demand for news videos online, with more consumers preferring to watch the news than read or listen to it. On the publisher side, there is a growing effort to use video summarization technology in order to create easy-to-consume previews (trailers) for different types of broadcast programs. How can we measure the quality of video summaries and their potential to misinform? This workshop will inform participants about automatic video summarization algorithms and how to produce more “representative” video summaries. The research presented is from the FAIRview project and is supported by the Digital News Innovation Fund (DNI Fund), which is part of the Google News Initiative.
FAIRview: Responsible Video Summarization @NYCML'18
FAIRview: Responsible Video Summarization @NYCML'18
Lora Aroyo
a joint demonstrator plan within the #responsible #datascience program @NLeSC @NWO https://www.linkedin.com/company/netherlands-escience-center/
Understanding bias in video news & news filtering algorithms
Understanding bias in video news & news filtering algorithms
Lora Aroyo
Keynote at Narrative Matters 2018 Lora Aroyo
StorySourcing: Telling Stories with Humans & Machines
StorySourcing: Telling Stories with Humans & Machines
Lora Aroyo
Video: https://youtu.be/9jlCJULSrhc --> Video with slides: https://av-media.vu.nl/VUMedia/Play/5745f2482d3f4fe7a547458393af322a1d Inaugural speech by Lora Aroyo, Vrije Universiteit Amsterdam Human-Computer Interaction chair
Data Science with Humans in the Loop
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Lora Aroyo
https://dhbenelux2017.eu/programme/keynotes/lora/
Digital Humanities Benelux 2017: Keynote Lora Aroyo
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Lora Aroyo, Chiel van den Akker, Marnix van Berchum, Lodewijk Petram, Gerard Kuys, Tommaso Caselli, Jacco van Ossenbruggen, Victor de Boer, Sabrina Sauer, Berber Hagedoorn
DH Benelux 2017 Panel: A Pragmatic Approach to Understanding and Utilising Ev...
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Lora Aroyo
The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to the volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, this assumption often creates issues in practice. Previous experiments we performed found that inter-annotator disagreement is usually never captured, either because the number of annotators is too small to capture the full diversity of opinion, or because the crowd data is aggregated with metrics that enforce consensus, such as majority vote. These practices create artificial data that is neither general nor reflects the ambiguity inherent in the data. To address these issues, we proposed the method for crowdsourcing ground truth by harnessing inter-annotator disagreement. We present an alternative approach for crowdsourcing ground truth data that, instead of enforcing an agreement between annotators, captures the ambiguity inherent in semantic annotation through the use of disagreement-aware metrics for aggregating crowdsourcing responses. Based on this principle, we have implemented the CrowdTruth framework for machine-human computation, that first introduced the disagreement-aware metrics and built a pipeline to process crowdsourcing data with these metrics. In this paper, we apply the CrowdTruth methodology to collect data over a set of diverse tasks: medical relation extraction, Twitter event identification, news event extraction and sound interpretation. We prove that capturing disagreement is essential for acquiring a high-quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with a majority vote, a method which enforces consensus among annotators. By applying our analysis over a set of diverse tasks we show that, even though ambiguity manifests differently depending on the task, our theory of inter-annotator disagreement as a property of ambiguity is generalizable.
Crowdsourcing ambiguity aware ground truth - collective intelligence 2017
Crowdsourcing ambiguity aware ground truth - collective intelligence 2017
Lora Aroyo
Ambiguity in interpreting signs is not a new idea, yet the vast majority of research in machine interpretation of signals such as speech, language, images, video, audio, etc., tend to ignore ambiguity. This is evidenced by the fact that metrics for quality of machine understanding rely on a ground truth, in which each instance (a sentence, a photo, a sound clip, etc) is assigned a discrete label, or set of labels, and the machine’s prediction for that instance is compared to the label to determine if it is correct. This determination yields the familiar precision, recall, accuracy, and f-measure metrics, but clearly presupposes that this determination can be made. CrowdTruth is a form of collective intelligence based on a vector representation that accommodates diverse interpretation perspectives and encourages human annotators to disagree with each other, in order to expose latent elements such as ambiguity and worker quality. In other words, CrowdTruth assumes that when annotators disagree on how to label an example, it is because the example is ambiguous, the worker isn’t doing the right thing, or the task itself is not clear. In previous work on CrowdTruth, the focus was on how the disagreement signals from low quality workers and from unclear tasks can be isolated. Recently, we observed that disagreement can also signal ambiguity. The basic hypothesis is that, if workers disagree on the correct label for an example, then it will be more difficult for a machine to classify that example. The elaborate data analysis to determine if the source of the disagreement is ambiguity supports our intuition that low clarity signals ambiguity, while high clarity sentences quite obviously express one or more of the target relations. In this talk I will share the experiences and lessons learned on the path to understanding diversity in human interpretation and the ways to capture it as ground truth to enable machines to deal with such diversity.
My ESWC 2017 keynote: Disrupting the Semantic Comfort Zone
My ESWC 2017 keynote: Disrupting the Semantic Comfort Zone
Lora Aroyo
Software systems are becoming ever more intelligent and more useful, but the way we interact with these machines too often reveals that they don’t actually understand people. Knowledge Representation and Semantic Web focus on the scientific challenges involved in providing human knowledge in machine-readable form. However, we observe that various types of human knowledge cannot yet be captured by machines, especially when dealing with wide ranges of real-world tasks and contexts. The key scientific challenge is to provide an approach to capturing human knowledge in a way that is scalable and adequate to real-world needs. Human Computation has begun to scientifically study how human intelligence at scale can be used to methodologically improve machine-based knowledge and data management. My research is focusing on understanding human computation for improving how machine-based systems can acquire, capture and harness human knowledge and thus become even more intelligent. In this talk I will show how the CrowdTruth framework (http://crowdtruth.org) facilitates data collection, processing and analytics of human computation knowledge. Some project links: - http://controcurator.org/ - http://crowdtruth.org/ - http://diveproject.beeldengeluid.nl/ - http://vu-amsterdam-web-media-group.github.io/linkflows/
Data Science with Human in the Loop @Faculty of Science #Leiden University
Data Science with Human in the Loop @Faculty of Science #Leiden University
Lora Aroyo
This was my talk at the Dutch #MediaInnovators event at the SXSW2017, in a session together with #SoundandVision #VPRO and #VARA
SXSW2017 @NewDutchMedia Talk: Exploration is the New Search
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Lora Aroyo
Presentation at the Annual Europeana meeting
Europeana GA 2016: Harnessing Crowds, Niches & Professionals in the Digital Age
Europeana GA 2016: Harnessing Crowds, Niches & Professionals in the Digital Age
Lora Aroyo
guest lecture at Knowledge & Media course 2016 VU University Amsterdam
"Video Killed the Radio Star": From MTV to Snapchat
"Video Killed the Radio Star": From MTV to Snapchat
Lora Aroyo
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NeurIPS2023 Keynote: The Many Faces of Responsible AI.pdf
NeurIPS2023 Keynote: The Many Faces of Responsible AI.pdf
CATS4ML Data Challenge: Crowdsourcing Adverse Test Sets for Machine Learning
CATS4ML Data Challenge: Crowdsourcing Adverse Test Sets for Machine Learning
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Harnessing Human Semantics at Scale (updated)
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Data excellence: Better data for better AI
CHIP Demonstrator presentation @ CATCH Symposium
CHIP Demonstrator presentation @ CATCH Symposium
Semantic Web Challenge: CHIP Demonstrator
Semantic Web Challenge: CHIP Demonstrator
The Rijksmuseum Collection as Linked Data
The Rijksmuseum Collection as Linked Data
Keynote at International Conference of Art Libraries 2018 @Rijksmuseum
Keynote at International Conference of Art Libraries 2018 @Rijksmuseum
FAIRview: Responsible Video Summarization @NYCML'18
FAIRview: Responsible Video Summarization @NYCML'18
Understanding bias in video news & news filtering algorithms
Understanding bias in video news & news filtering algorithms
StorySourcing: Telling Stories with Humans & Machines
StorySourcing: Telling Stories with Humans & Machines
Data Science with Humans in the Loop
Data Science with Humans in the Loop
Digital Humanities Benelux 2017: Keynote Lora Aroyo
Digital Humanities Benelux 2017: Keynote Lora Aroyo
DH Benelux 2017 Panel: A Pragmatic Approach to Understanding and Utilising Ev...
DH Benelux 2017 Panel: A Pragmatic Approach to Understanding and Utilising Ev...
Crowdsourcing ambiguity aware ground truth - collective intelligence 2017
Crowdsourcing ambiguity aware ground truth - collective intelligence 2017
My ESWC 2017 keynote: Disrupting the Semantic Comfort Zone
My ESWC 2017 keynote: Disrupting the Semantic Comfort Zone
Data Science with Human in the Loop @Faculty of Science #Leiden University
Data Science with Human in the Loop @Faculty of Science #Leiden University
SXSW2017 @NewDutchMedia Talk: Exploration is the New Search
SXSW2017 @NewDutchMedia Talk: Exploration is the New Search
Europeana GA 2016: Harnessing Crowds, Niches & Professionals in the Digital Age
Europeana GA 2016: Harnessing Crowds, Niches & Professionals in the Digital Age
"Video Killed the Radio Star": From MTV to Snapchat
"Video Killed the Radio Star": From MTV to Snapchat
NoTube: Recommendations @ Korea Telecom
1.
2.
3.
Difficult to Choose
What to Watch
4.
5.
User’s Perspective
in Linked Data
6.
Generating of Explanations
7.
DBPedia Categories Find
common DBPedia Categories between media items resources & user profile
8.
Interest Categories
9.
10.
11.
Linked Data Patterns:
Example
12.
13.
Acknowledgements
Download now