UMN-CSCI5123-RecommenderSystem:UMNCSCI5123RecommenderSystems课程笔记资源-CSDN文库

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md：19个

mhtml：2个

html：2个

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UMN-CSCI5123-RecommenderSystem-master.zip （28个子文件）

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UMN-CSCI5123-RecommenderSystem-master

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Coursera

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Introduction to Recommender Systems - Non-Personalized and Content-Based

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LectureNotes

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Week 1

10. Introducing the Honors Track.md 775B

3. Movielens Tour.md 56B

5. Predictions and Recommendations.md 967B

8. Tour of Amazon.md 2KB

9. Recommender Systems - Past, Present, and Future.md 2KB

6 & 7. Taxonomy of Recommenders.md 4KB

2. Intro to Course and Specialization.md 249B

1. Intro to Recommender Systems.md 2KB

4. Preferences and Ratings.md 1KB

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Week 3

1. Introduction to Content-Based Recommenders.md 6KB

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Week 2

UX Research_ 5 Requirements for the ‘Ratings Distribution Summary’ on the Product Page (65% Get it Wrong) – Articles – Baymard Institute.mhtml 2.03MB

4. Demographics and Related Approaches.md 3KB

How Not To Sort By Average Rating – Evan Miller.mhtml 286KB

1. Non-Personalized and Stereotype-Based Recommenders.md 859B

Poll.png 255KB

NotesOfAdditionalReading.md 5KB

5. Product Association Recommenders.md 2KB

How Reddit ranking algorithms work _ by Amir Salihefendic _ Hacking and Gonzo _ Medium.html 682KB

2 & 3. Summary Statistics.md 6KB

How Hacker News ranking algorithm works _ by Amir Salihefendic _ Hacking and Gonzo _ Medium.html 673KB

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HonersTrack

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Week 1

11. Setting up the development environment.md 651B

Getting_Started_with_LensKit_for_Python.ipynb 45KB

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Week 2

Non-Personalized Recommender Assignment.pdf 120KB

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Notes

Metrics.md 161B

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MainCourse

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Assignments

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Homework1

README.md 382B

requirements.txt 14B

.gitignore 1KB

README.md 11KB

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