Post #179
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Channel Public Channel
CO Collective Intelligence
@co_intelligence
Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.
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Post #178
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Post #177
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Post #176
35
Four projects in the intellectual history of quantitative social science
1. The rise and fall of game theory.
2. The disaster that is “risk aversion.”
3. From model-based psychophysics to black-box social psychology experiments.
4. The two models of microeconomics.
https://statmodeling.stat.columbia.edu/2020/01/12/four-projects-in-the-intellectual-history-of-quantitative-social-science/
1. The rise and fall of game theory.
2. The disaster that is “risk aversion.”
3. From model-based psychophysics to black-box social psychology experiments.
4. The two models of microeconomics.
https://statmodeling.stat.columbia.edu/2020/01/12/four-projects-in-the-intellectual-history-of-quantitative-social-science/
Post #175
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Post #174
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Post #173
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Post #172
42
List of publications about the intersection UX and ML in applied settings: https://mounia-lalmas.blog/publications/
From the Lab to the Market Publications Publications 2024 A Damianou, F Fabbri, P Gigioli, M De Nadai, A Wang, E Palumbo & M Lalmas. Towards Graph Foundation Models for Personalization, The Web Conference (Graph Foundation Models Wor… Post #171
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The focus is how to identify what success means so that the machine learning algorithm (in this case based on multi-armed contextual bandits) captures that users differ into how they listen to music and playlist consumption varies a lot.
https://mounia-lalmas.blog/2018/10/11/personalizing-the-user-experience-and-playlist-consumption-on-spotify/
https://mounia-lalmas.blog/2018/10/11/personalizing-the-user-experience-and-playlist-consumption-on-spotify/
Post #170
35
Taxonomy is a methodology that classifies entities and defines the hierarchical relationship among them. It’s widely used as a knowledge management system in the industry, and has proven success in improving the accuracy of the machine learning models in search, user-behavior modeling, and classification tasks.
https://medium.com/@Pinterest_Engineering/interest-taxonomy-a-knowledge-graph-management-system-for-content-understanding-at-pinterest-a6ae75c203fd
https://medium.com/@Pinterest_Engineering/interest-taxonomy-a-knowledge-graph-management-system-for-content-understanding-at-pinterest-a6ae75c203fd
Post #169
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Post #168
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Post #167
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16 years ago the authors signed off with this thought:
Agents might eventually be fellow team members with humans in the way a young child or a novice can be – subject to the consequences of brittle and literal-minded interpretation of language and events, limited ability to appreciate or even attend effectively to key aspects of the interaction, poor anticipation, and insensitivity to nuance.
We’ve still got a long way to go…
http://blog.acolyer.org/2020/01/10/ten-challenges-for-automation/
Agents might eventually be fellow team members with humans in the way a young child or a novice can be – subject to the consequences of brittle and literal-minded interpretation of language and events, limited ability to appreciate or even attend effectively to key aspects of the interaction, poor anticipation, and insensitivity to nuance.
We’ve still got a long way to go…
http://blog.acolyer.org/2020/01/10/ten-challenges-for-automation/
Post #166
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Post #165
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Post #164
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Post #163
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Post #162
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Modeling Human Values with Social Media
https://sites.google.com/site/ic2s2humanvalues/
https://www.slideshare.net/YelenaMejova/modeling-human-values-with-social-media
Google Modeling Human Values with Social Media Tutorial at the International Conference on Computational Social Science
Modeling Human Values with Social Media slides available on Slideshare https://sites.google.com/site/ic2s2humanvalues/
https://www.slideshare.net/YelenaMejova/modeling-human-values-with-social-media
Post #161
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*Causal Inference in Online Systems - Tutorial*
https://github.com/amit-sharma/causal-inference-tutorial
https://kellogg-northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7ebafe1f-8af3-46a2-8308-a909014772c0
https://github.com/amit-sharma/causal-inference-tutorial
https://kellogg-northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7ebafe1f-8af3-46a2-8308-a909014772c0
Post #160
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Interacting with Recommenders – Overview and Research Directions
https://web-ainf.aau.at/pub/jannach/files/Journal_TiiS_2017.pdf
Evaluating Recommender Systems with User Experiments
https://www.usabart.nl/portfolio/KnijnenburgWillemsen-UserExperiments.pdf
User Perception of Next-Track Music Recommendations
https://web-ainf.aau.at/pub/jannach/files/Conference_UMAP_2017.pdf
https://web-ainf.aau.at/pub/jannach/files/Journal_TiiS_2017.pdf
Evaluating Recommender Systems with User Experiments
https://www.usabart.nl/portfolio/KnijnenburgWillemsen-UserExperiments.pdf
User Perception of Next-Track Music Recommendations
https://web-ainf.aau.at/pub/jannach/files/Conference_UMAP_2017.pdf