Nearest Neighbor Collaborative Filtering
Master user-user and item-item collaborative filtering techniques for personalized recommendation systems.
Master user-user and item-item collaborative filtering techniques for personalized recommendation systems.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Recommender Systems Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.3
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Implement user-user collaborative filtering algorithms
Develop item-item collaborative filtering systems
Understand similarity metrics and rating normalization
Handle cold-start problems in recommendation systems
Implement influence limiting and attack resistance measures
Create trust-based recommendation systems
Skills you'll gain
This course includes:
4.2 Hours PreRecorded video
7 assignments
Access on Mobile, Desktop, Tablet
FullTime access
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There are 6 modules in this course
This comprehensive course focuses on nearest-neighbor techniques for creating personalized recommendations. Students learn both user-user and item-item collaborative filtering algorithms, exploring their implementation, variations, benefits, and limitations. The course covers essential concepts like similarity metrics, rating normalization, and prediction algorithms, while also addressing advanced topics such as influence limiting, attack resistance, and trust-based recommendations. Through hands-on assignments and programming exercises, students gain practical experience in implementing these algorithms.
Preface
Module 1 · 13 Minutes to complete
User-User Collaborative Filtering Recommenders Part 1
Module 2 · 1 Hours to complete
User-User Collaborative Filtering Recommenders Part 2
Module 3 · 4 Hours to complete
Item-Item Collaborative Filtering Recommenders Part 1
Module 4 · 1 Hours to complete
Item-Item Collaborative Filtering Recommenders Part 2
Module 5 · 4 Hours to complete
Advanced Collaborative Filtering Topics
Module 6 · 1 Hours to complete
Fee Structure
Instructors
Leading Expert in Human-Computer Interaction at the University of Minnesota
Dr. Joseph A. Konstan is a distinguished professor in the Department of Computer Science and Engineering at the University of Minnesota, where he holds the title of Distinguished McKnight University Professor and Distinguished University Teaching Professor. His extensive research focuses on human-computer interaction, particularly in the areas of recommender systems, social computing, and public health applications. Notably, his work on the GroupLens Recommender System earned him the prestigious ACM Software Systems Award in 2010.Dr. Konstan received his A.B. from Harvard University and both his M.S. and Ph.D. from the University of California, Berkeley. He is recognized for his contributions to education through various teaching awards and has delivered numerous webinars and short courses on topics such as recommender systems and ethical issues in social computing. As a Fellow of the ACM, IEEE, and AAAS, he has also served as President of ACM SIGCHI and has chaired several major conferences in the field. His courses on Coursera include "Introduction to Recommender Systems," "Evaluating User Interfaces," and "User Research and Design," aimed at equipping students with essential skills in user experience and system design.
Expert in Recommender Systems at Boise State University
Dr. Michael D. Ekstrand is an Assistant Professor in the Department of Computer Science at Boise State University, where he focuses on evaluating and understanding recommender systems in relation to user goals and information needs. His research emphasizes supporting reproducible research in the field of recommender systems. Dr. Ekstrand is also the lead developer of LensKit, an open-source toolkit designed for building, researching, and studying recommender systems.He teaches several courses on Coursera, including "Introduction to Recommender Systems: Non-Personalized and Content-Based," "Matrix Factorization and Advanced Techniques," and "Recommender Systems Capstone." His work aims to enhance the effectiveness of recommendation algorithms while ensuring they meet user needs.Dr. Ekstrand earned his Ph.D. from the University of Minnesota and has contributed significantly to the field through his research and development efforts. He is actively involved in various academic initiatives and has presented at numerous conferences related to information science and recommender systems.
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4.3 course rating
304 ratings
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