Recommendation System

1. Recommendation System for
Books
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Under the Guidance of
Prof. Praveen M D
Name USN
Abhishek M G 2BV12IS002
Rashmi N T 2BV12IS047
Sachin Patil 2BV12IS077
Rishabh Mehta 2BV12IS071

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3. Function Means Tree
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Decision tree
matrix
Euclidean distance

4. Dataset
• 278,858 users
• 1,149,780 ratings
• 271,379 books
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5. Why KNN?
• Non parametric
• Decision based on complete data
kNN Versus SVM in the Collaborative Filtering
Framework
Miha Grcar Jozef Stefan Institute Jamova
391000 Ljubljana, Slovenia miha.grcar@ijs.si
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6. Contents
• Problem Statement
• Introduction
• Existing System
• System Design
• OFCM
• System Evaluation
• Course relevance
• References
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7. 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.
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Problem Statement

8. Introduction
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9. 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
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Introduction (Contd..)

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A recommendation system...
how its work?
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Introduction (Contd..)

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Recommender system (RS) help users
find items (e.g., news items,
movies,Books) that meet their
specific needs.
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Introduction (Contd..)

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2 Common Approaches :
1.Collaborative filtering
2.Content-based filtering
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Recommendation System

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Based on a description of the item and a profile of
the user’s preference (Brusilovsky Peter , 2007)
Content Based Filtering
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14. • Need to know about item content
– requires manual or automatic indexing
– Item features do not capture everything
Limitations of Traditional
Recommendation system:
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• Lack of serendipity
[Wikipedia: “the effect by which one accidentally
discovers something fortunate, especially while
looking for something entirely unrelated” ]

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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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System Design
Recommendation System for Books

17.  User-based collaborative filtering
 Item-based collaborative filtering
Types Of Collaborative Filtering
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18. User based
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19. 19
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
1 2 3 4 5 6
1
2
3
4
5
6
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Item Similarity Calculation
1
1 2 3 4 5 6
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33. 33
Similar Item
1
4 3 6 5 2
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34. Function Means Tree
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35. • MAE—Mean Absolute Error
• RMSE--Root mean squared error
System Evaluation
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Course Relevance
Sl No : Concept Used Subject
1 Data preprocessing, classification,
Prediction.
Data Mining,
Machine Learning
2 PHP,HTML5 Web Technology
3 Similarity Computation. Linear Algebra
4 Collaborative filtering algorithms D.S & Algorithms

37. References
FRANCESCO RICCI .(2010). Recommender Systems Handbook.
LONDON:SPRINGER.
ALA ALLUHAIDAN. Recommender System Using Collaborative
Filtering Algorithm.
JOONSEOK LEE.(2012).A Comparative Study of Collaborative
Filtering Algorithms.
B.M. Sarwar et al., “Item-Based Collaborative Filtering
RecommendationAlgorithms,” 10th Int’l World Wide Web
Conference, ACM Press, 2001, pp. 285-295.
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38. Thank You !
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