Collaborative Filtering
Collaborative filtering is a recommendation system that identifies similarities between users to suggest preferences rather than relying on item content. Its growing adoption in online retailing, such as Amazon, and TV programming, like TiVo, highlights its effectiveness in improving recommendation quality. By utilizing methods like mean values, Pearson correlations, and singular value decomposition (SVD), collaborative filtering enhances accuracy and breadth of recommendations. Despite its advantages, challenges such as computational intensity and the necessity for user preference tracking remain.
Collaborative Filtering
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Presentation Transcript
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? • Improves recommendation quality • Overcomes limitations of existing systems • Breadth of recommendations • Limitations on item content • Dependence on dense preference data
Filtering Methods • Mean Values • Pearson Correlations • Singular Value Decomposition
Mean Values • Simple Approach • Weighted average of others’ preferences for an item • Normalize prediction
Pearson Correlations • Find similar users • Weight by level of similarity • Predict based on their prefs
Singular Value Decomposition • Linear Algebra approach • Breaks down items by features • Predicts based on responses to these features
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
Preliminary Results • Mean Values: • Pearson: 0.1705 • SVD: • Vector-Space: • Mean Squared Difference: • Cosine: • Mean User:
