recommendation system
TRANSCRIPT
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Book Recommendation System
Under the Guidance ofProf. Praveen M D
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Contents
• Introduction• Problem Statement• Existing System• System Design• Methodology• System Evaluation• Course relevance• References
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Introduction
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Are they effective??(Celma & Lamere, ISMIR 2007)
Netflix 2/3 rated movies are from recommendation Google News 38% more click-through are due tommendation Amazon 35% sales are from recommendation
Introduction (Contd..)
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A recommendation system...how its work?
Introduction (Contd..)
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Recommender system (RS) help users find items (e.g., news items,
movies,Books) that meet their specific needs.
Introduction (Contd..)
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To recommend top-N most relevant books for a user, using item based collaborative filtering & user based collaborative filtering techniques and evaluating the performance of these two techniques.
Problem Statement
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3 Common Approaches
1.collaborative filtering2.content-based filtering3.hybrid recommender system
Recommendation System
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Based on a description of the item and a profile ofthe user’s preference (Brusilovsky Peter , 2007)
Content Based Filtering
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A method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste
information from many users (collaborating)
Collaborative Filtering
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• Need to know about item content – requires manual or automatic indexing – Item features do not capture everything
• “User cold-start” problem – Needs to learn what content features are important for the user, so takes time
• What if user’s interests change?
Problems with Content Based Filtering:
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• Lack of serendipity [Wikipedia: “the effect by which one accidentally discovers something fortunate, especially while looking for something entirely unrelated” ]
Problems with Content Based Filtering:
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System Design
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• User-based collaborative filtering
• Item-based collaborative filtering
Types Of Collaborative Filtering
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User & Item
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Order Data
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Order Data (Cont.)
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Order Data (Cont.)
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Similarity Calculation
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Similarity Calculation
Pearson’s Correlation
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Similarity Calculation Example
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K-Nearest Neighbor
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K-Nearest Neighbor
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Neighbor’s Ratings
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Remove Rated Items
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Calculating Final Score
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Item Similarity Calculation
Adjusted Cosine Similarity
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Item Similarity Calculation Example
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Item Similarity Calculation
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Similar Item
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• MAE—Mean Absolute Error
• RMSE--Root mean squared error
System Evaluation
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Course Relevance
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THANK YOU