https://en.wikipedia.org/wiki/Ontology_(information_science)

In computer science and information science, an ontology encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse.

Every field creates ontologies to limit complexity and organize information into data and knowledge. As new ontologies are made, their use hopefully improves problem solving within that domain. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages.[1]

Since Google started an initiative called Knowledge Graph in 2012, a substantial amount of research has used the phrase knowledge graph as a generalized term. Although there is no clear definition for the term knowledge graph, it is sometimes erroneously used as synonym for ontology.[2] One common interpretation is that a knowledge graph represents a collection of interlinked denoscriptions of entities – real-world objects, events, situations or abstract concepts.[3] Unlike ontologies, knowledge graphs, such as Google's Knowledge Graph, often contain large volumes of factual information with less formal semantics. In some contexts, the term knowledge graph is used to refer to any knowledge base that is represented as a graph.

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

*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

Modeling Human Values with Social Media

https://sites.google.com/site/ic2s2humanvalues/
https://www.slideshare.net/YelenaMejova/modeling-human-values-with-social-media

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/

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

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/

List of publications about the intersection UX and ML in applied settings: https://mounia-lalmas.blog/publications/

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/