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Collaborative Filtering

Collaborative Filtering. Todor Kalaydjiev Paul Rosania. What is it?. Recommendation system Discovers similarities between users Recommends based on preferences, not content Growing adoption Online retailing (e.g. Amazon) TV programming (e.g. TiVo). Why use it?.

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Collaborative Filtering

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  1. Collaborative Filtering • Todor Kalaydjiev • Paul Rosania

  2. What is it? • Recommendation system • Discovers similarities between users • Recommends based on preferences, not content • Growing adoption • Online retailing (e.g. Amazon) • TV programming (e.g. TiVo)

  3. Why use it? • Improves recommendation quality • Overcomes limitations of existing systems • Breadth of recommendations • Limitations on item content • Dependence on dense preference data

  4. Filtering Methods • Mean Values • Pearson Correlations • Singular Value Decomposition

  5. Mean Values • Simple Approach • Weighted average of others’ preferences for an item • Normalize prediction

  6. Pearson Correlations • Find similar users • Weight by level of similarity • Predict based on their prefs

  7. Singular Value Decomposition • Linear Algebra approach • Breaks down items by features • Predicts based on responses to these features

  8. Summary • Advantages over content-based approach • Increased accuracy • Broader applications • Disadvantages • Computationally intensive • Requires intensive user preference tracking • Cannot work without some established rating correlations

  9. Preliminary Results • Mean Values: • Pearson: 0.1705 • SVD: • Vector-Space: • Mean Squared Difference: • Cosine: • Mean User:

  10. Final Results

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