Recommender systems are the software tools that make valuable recommendations to users by considering their profiles, preferences during interaction usually with online applications or websites. Please kindly contact me at Sharma_binay9@hotmail.com for any questions. Thank you.

2. Thesis on Online BookStore
Recommender System using
Collaborative Filtering
Algorithm
Binay Kumar Sharma
European University Cyprus

3. Overview
1. Introduction of RS
2. Types and Techniques of RS
3. Existing RS Systems and challenges of
RS
4. Online Bookstore RS
5. Architecture and Similarity Method of
RS
6. Algorithms and Interface of RS
7. Evaluation Metrics of RS
8. Conclusion and Future work

4. Recommender
Systems
Introduction

5. Generic
Model
of RS

6. Is about
Users
Ratings
Items

7. Recommender
System
Help user find item
of their interest.

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According to review, analysis, compare and implement of RS ,
it is difficult for RS to offer suggestions to new
users as their user’s profile is practically unfilled
and they have not been appraised any items yet
So, their taste is obscure to the systems
Problem Descriptions

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 to reviews of different types, techniques and
methods of RS
 to implement a sample prototype i.e. Bookstore RS Apps
using CF techniques and evaluations of RS
the new user’s profiles which have taken into their
account to provide recommendations
Objectives of Research

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RS Techniques:
Collaborative Filtering Techniques
RS Similarity Methods:
 Pearson Correlation,
and Euclidean Distance
Proposed Techniques and Methods

11. Types
of RS

12. Recommender
Systems
Functions

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 Collection of the Data: Explicit and Implicit data
 Storage of the Data: Standard storage Movielens
and Book Crossing dataset
 Filtering of the Data: To make recommendation using
RS filtering technique
Recommender Systems Functions

14. Recommender
Systems Techniques
were Reviewed

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 Content based filtering for domains
 Based on the user’s preferences by utilizing
features exhausted from content of users and items
Content Based Filtering Technique

16. Content
based
Filtering
Technique
Examples

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 To match this user with similar interest by obtaining
the similarities among the users’ preference
 To make recommendations based on their
profile.
Collaborative Filtering Technique

18. Collaborative
Filtering
Technique
Examples

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1. Memory based collaborative filtering
2. Model based collaborative filtering
3. Hybrid based collaborative filtering
Types of Collaborative Filtering

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 Two techniques such as user-based and item-based
collaborative filtering technique.
- Similarity based model
- Use entire collection of previously rate items by
the user
- Store all user information in a database
Memory Based Filtering Technique

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 Provides recommendations for particular user
 Based on user’s similarity to other users
 Similarity defined through the items –users preferred
or not.
 People who like a lot of the same items you like
also like this other items.
User Based CF Technique

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 For this technique, similarity between items is
taken into account rather than users.
 Also depend on this similarity, users’ preferences for
item hasn’t been already rated by the user can also be
calculated.
Item Based CF Technique

23. Memory
based
CF
Technique
Examples

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 Use collection of rating to learn model which is used to
make rating prediction
Types of this model:
1. Clustering Technique
2. Association Technique
3. Bayesian Technique
4. Neural Network Technique
Model Based CF Technique

25. Hybrid
Filtering
Technique

26. The
Architecture of
RS Systems

27. Architecture
of RS

28. The
Similarity Methods of
RS Systems

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 Pearson Coefficient
 Euclidean Distance
 Cosine Similarity
 Popularity Based Ranking
Similarity Methods of RS Systems

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Block Diagram of Similarity Method of RS

31. The
Comparisons of
existing RS Systems

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 The time complexity of the algorithm
-Computational complexity
 Efficiency of the techniques
 Correctness/accuracy of ranking
Comparison of existing RS Systems

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 Relevance: that items, recommended are relevant
to the users
 Novelty: that the item recommended is totally new
to the user
 Serendipity: the item recommended are even
though relevant
Comparison of existing RS Systems

34. The
Challenges of
RS Systems

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 Data Sparsity: Large amount of items in
dataset only a few have rating
 Scalability: Large amount of existing users
and items in collaborative filtering algorithms.
 Cold Start Problem: New user and new
item is introduced in the dataset.
Challenges of RS Systems

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 Shilling Attacks: Large number of people where
competitors are given negative
recommendations
 Privacy: People may not want their views and
opinions to be publicized in collaborative filtering systems.
 Grey Sheep: Users whose preferences happens to be
in consistent conflict with any group of people
Challenges of RS Systems

37. The
Algorithms of
RS Systems

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 Content Based Filtering Algorithm
 Collaborative Filtering Algorithm(Used)
 Hybrid Filtering Algorithm
Algorithms of RS Systems

39. The
Methodology of
RS Systems

40. n
How the RS is Implemented
Visualization
Dataset
from DB
User’s
Item’s
Web AppDataset Controller(RS)
RS that filters
all of it

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Implementation of Algorithms(Jupyter)
Please Double Click on it

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Imported Dataset from Database
Please Double Click on it

43. The
Evaluation Metrics of
RS Systems

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MRR and RMSE Metrics for Evaluation

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Another Metrics for Evaluation

46. The
Implementation of
RS Systems

47. We implement
an
Online Bookstore
Recommender Systems

48. About Bookstore RS ?What

49. It is an Internet Based
software application
And Personalized
Information filtering
technique

50. Requests
of user’s
preference
BRS System
Calculates
Similarity via
Popular and
Pearson’s
correlation
To find
recommend
ed items or
products
Response
by server
results of
CF
Web app

51. Register
User
Login
Search
Popular
items
Select
and read
items
Review
and List
items
Rating
Items

52. Home
Page

53. Login
Page

54. Dashboard
Page

55. Book
Search
Page

56. Ratings
Page

57. Result
Page

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 The RS is a valuable software tools
 Therefore, it is solved by collaborative
filtering algorithm.
 The most popular recommendations show to different
users’ preferences by using similarities
In Conclusion

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In the future work, we will use cluster based hybrid
collaborative filtering technique
for the best performance and solutions in
recommender systems.
In Future

60. Please Watch Demo Here!!!

61. Thanks for your Attention!