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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