Learn · Recommender Systems

Lecture 40 of 44week-08

Lecture 40: Trust aware recommender systems

Prof. Mamata Jenamani · IIT Kharagpur

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From
Recommender Systems, IIT Kharagpur (at NPTEL)
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures

  1. Lecture 1: Recommender system and its business value Video
  2. Lecture 2: Types of recommender system-I Video
  3. Lecture 3: Types of recommender system-II Video
  4. Lecture 4: Data Collection Video
  5. Lecture 5: Data Description Video
  6. Lecture 06: Data preprocessing Video
  7. Lecture 07: Dimensionality Reduction Video
  8. Lecture 08: Introduction to machine learning-I Video
  9. Lecture 09: Introduction to machine learning-II Video
  10. Lecture 10: Introduction to machine learning-III Video
  11. Lecture 11: Distance and Similarity Video
  12. Lecture 12: Distance and Similarity (Continued) Video
  13. Lecture 13: User-Based Approach Video
  14. Lecture 14: Item-Based Approach Video
  15. Lecture 15: Additional Topics in Neighbourhood Based Approach Video
  16. Lecture 16: Association rule based model Video
  17. Lecture 17: UV Decomposition Video
  18. Lecture 18: The latent factor model Video
  19. Lecture 19: Basic latent factor models Video
  20. Lecture 20: Other advanced models Video
  21. Lecture 21: Introduction to content basedrecommender system: Foundations Video
  22. Lecture 22: Feature Engineering - I Video
  23. Lecture 23: Feature Engineering - II Video
  24. Lecture 24: Feature Engineering - III Video
  25. Lecture 25: Feature Engineering - IV Video
  26. Lecture 26: Decision Trees for content based recommendation Video
  27. Lecture 27: Naïve Bayes classifier for content based recommendation Video
  28. Lecture 28: kNN Classifier for Recommender System Video
  29. Lecture 29: Rule based classification Video
  30. Lecture 30: Regression methods and conclusions Video
  31. Lecture 31: Introduction to evaluation ofrecommender system Video
  32. Lecture 32: Resampling methods Video
  33. Lecture 33: Evaluation metrics for accuracy Video
  34. Lecture 34: Drawing reliable conclusions-I Video
  35. Lecture 35: Drawing reliable conclusions-II Video
  36. Lecture 36: Hybrid recommender systems Video
  37. Lecture 37: Knowledge based recommender systems Video
  38. Lecture 38: Context-Sensitive recommender systems Video
  39. Lecture 39: Structural Recommendations in Networks Video
  40. Lecture 40: Trust aware recommender systems Video
  41. Live Session 19-03-2024 Video
  42. Live Session 15-04-2024 Video
  43. Live Session 03-03-2025 Video
  44. Live Session 03-04-2025 Video