This document provides an overview of a book recommendation system project. It introduces the problem of recommending books to users and discusses existing recommendation approaches like collaborative and content-based filtering. It then outlines the design of the system, which will use both user-based and item-based collaborative filtering techniques. It describes how these techniques work by calculating item and user similarities, identifying nearest neighbors, and making predictions. Finally, it discusses how the system will be evaluated using metrics like mean absolute error and root mean squared error.

1. Book Recommendation
System
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Under the Guidance of
Prof. Praveen M D

2. Contents
• Introduction
• Problem Statement
• Existing System
• System Design
• Methodology
• System Evaluation
• Course relevance
• References
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3. Introduction
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4. 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..)

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

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3 Common Approaches
1.collaborative filtering
2.content-based filtering
3.hybrid recommender system
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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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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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11. • 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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12. • Lack of serendipity
[Wikipedia: “the effect by which one accidentally
discovers something fortunate, especially while
looking for something entirely unrelated” ]
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Problems with Content Based Filtering:

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System Design

14. • User-based collaborative filtering
• Item-based collaborative filtering
Types Of Collaborative Filtering
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15. 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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31. • MAE—Mean Absolute Error
• RMSE--Root mean squared error
System Evaluation
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Course Relevance

33. THANK YOU
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