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Recommendation System for
Books
8/23/2015 1
Under the Guidance of
Prof. Praveen M D
Name USN
Abhishek M G 2BV12IS002
Rashmi N T 2BV12IS047
Sachin Patil 2BV12IS077
Rishabh Mehta 2BV12IS071
8/23/2015 2
Function Means Tree
8/23/2015 3
Decision tree
matrix
Euclidean distance
Dataset
• 278,858 users
• 1,149,780 ratings
• 271,379 books
8/23/2015 4
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
8/23/2015 5
Contents
• Problem Statement
• Introduction
• Existing System
• System Design
• OFCM
• System Evaluation
• Course relevance
• References
8/23/2015 6
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.
8/23/2015 7
Problem Statement
Introduction
8/23/2015 8
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
8/23/2015 9
Introduction (Contd..)
8/23/2015
A recommendation system...
how its work?
10
Introduction (Contd..)
8/23/2015
Recommender system (RS) help users
find items (e.g., news items,
movies,Books) that meet their
specific needs.
11
Introduction (Contd..)
8/23/2015
2 Common Approaches :
1.Collaborative filtering
2.Content-based filtering
12
Recommendation System
8/23/2015
Based on a description of the item and a profile of
the user’s preference (Brusilovsky Peter , 2007)
Content Based Filtering
13
• Need to know about item content
– requires manual or automatic indexing
– Item features do not capture everything
Limitations of Traditional
Recommendation system:
8/23/2015 14
• Lack of serendipity
[Wikipedia: “the effect by which one accidentally
discovers something fortunate, especially while
looking for something entirely unrelated” ]
8/23/2015
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
15
8/23/2015 16
System Design
Recommendation System for Books
 User-based collaborative filtering
 Item-based collaborative filtering
Types Of Collaborative Filtering
8/23/2015 17
User based
8/23/2015 18
19
Order Data
8/23/2015
20
Order Data (Cont.)
8/23/2015
21
Order Data (Cont.)
8/23/2015
22
Similarity Calculation
8/23/2015
23
Similarity Calculation
Pearson’s Correlation
8/23/2015
24
Similarity Calculation Example
8/23/2015
25
K-Nearest Neighbor
8/23/2015
268/23/2015
K-Nearest Neighbor
27
Neighbor’s Ratings
8/23/2015
28
Remove Rated Items
8/23/2015
29
Calculating Final Score
8/23/2015
30
Item Similarity Calculation
Adjusted Cosine Similarity
8/23/2015
31
Item Similarity Calculation Example
1 2 3 4 5 6
1
2
3
4
5
6
8/23/2015
32
Item Similarity Calculation
1
1 2 3 4 5 6
8/23/2015
33
Similar Item
1
4 3 6 5 2
8/23/2015
Function Means Tree
8/23/2015 34
• MAE—Mean Absolute Error
• RMSE--Root mean squared error
System Evaluation
8/23/2015 35
8/23/2015 36
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
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.
8/23/2015 37
Thank You !
8/23/2015 38
8/23/2015 39

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B7 ppt