1. MUSIC RECOMMENDATION SYSTEM FOR LAST.FM DATASET

2. Why music recommendation system is required?

3. What is a data mining ?
Data mining , which can be called data or knowledge discovery, is the process of analyzing data from different perspectives and summarizing it into useful information.

4. according to other classified label class
Data mining Modelling
Clustering Classification Association
Items are grouped for their similar specification in this method. It is consider the similarities of data among themselves
It is very common technique for predicting some interests. It may refer to categorization data items. Unclassified cases are predicted as any class label group
according to other classified label class
Existing records in the database by examining their relationship with each other, it is a technique that determines which events occur together simultaneously

5. What is recommendation engine?
Recommendation system is described as system which interprets data that users entered the system and makes recommendation to users.

6. Recommendation Techniques
Content-based Filtering
The salient features of any contents which were liked or watched previously by users are saved in mostly databases and new profile is created for users.
While making recommendation, the content that belongs to nearest feature from the sets of property previously created is recommended with looking at this profile.

7. Recommendation Techniques
Collaborative Filtering
This constitutes the foundation of “The one loving one loves the alike” approaches.
It is not depending on the one user's content- property profile, while making recommendation bearing in mind that users who like the similar content properties or users with similar characteristics.

8. Recommendation Techniques
Collaborative Filtering Types
User-based recommendation: This technique finds the similar users and recommends item.
Item-based recommendation: The similarity of items is calculated and items are recommended.

9. How to be created recommendation engine ?

10. How to be created recommendation engine ?
When the recommendation engine is created, the following steps should be implemented.
The definition of data representation
The creation of database or file model structure
Making data pre-processing for getting the best result

11. What is an Apache Mahout ?
It is a Java library of scalable machine-learning algorithms, implemented on top of Apache Hadoop and using the MapReduce paradigm.
For using Mahout in project:
Download the latest Mahout release is 0.8
It can be accessed from the link below
Extract all the libraries and include them in a new Eclipse (NetBeans) project as external JAR file.
Java 1.6.x or greater is required for installation
Hadoop is not mandatory to create recommendation engine.

12. How to use Mahout for recommendation?
The recommendation in Mahout follows these steps:
The dataset is adjusted for Mahout-compliant
The compatible recommender component is chosen
The similarity calculations are computing according to rating or preferences
The recommendation is evaluated

13. Recommender job flow
The main step doing the heavy lifting in the workflow is the "calculate co-occurrences" step. This step is responsible for doing pairwise comparisons across the entire matrix, looking for commonalities.

14. The background process of recommendation in architecture

15. Graduation Project with Last.fm
Scheduling

16. Graduation Project with Last.fm
Gannt chart

17. Graduation Project with Last.fm
What is important risks ?
Big-Data
Time
Computer performance
Sparsity

18. Music recommendation project for Last.fm
The dataset of « Last.fm Dataset-1K users » is used in project. This dataset has information about user properties and which songs are listened by which users.
This dataset 2 files, one of them is users’ profile file and other one contains users’ musical history.
There are 1000 users and 19,150,868 lines musical history which belongs to 1000-users.

19. Music recommendation project for Last.fm
Last.fm API is used and new csv format is created.
Although there are 1000 users, during to project period 700 users' files with desired properties were prepared due to time constraints.
After preparing files, all files were saved on database tables for the sake of easy data processing, the tables:
Artists
Tracks
UserTagTrack
Users
TrackTags

20. Music recommendation project for Last.fm
The collaborative filtering method is used.
2 types of segmentation are considered.
The one of the recommendation is made between clustering users according to gender, age, country type.
Other recommendation is made between all users.
User-based recommendation engine is created.
JDBC and File Data Model is used for data representation.

21. Music recommendation project for Last.fm
To make cluster, Weka is used because of simplicity. All users' characteristics were represented as value. (In thesis page 33-34)
goes
…….

22. Music recommendation project for Last.fm
There are many methods can be used for collaborative filtering :
Mean Squared Differences Algorithm
Vector Similarity
Pearson Correlation Coefficient
Strengths and Weaknesses of Collaborative Filtering Method
Pearson Correlation Similarity algorithm is used for thesis data model. Since it is convenient and gives correct result for huge amount of data.

23. The functionality of project system

24. JDBC Model-Database Tables
Artists
artist id
artist name
Tracks
track id
track name
artist id
published year
TrackTags
tag id
tag name
Users
user id
user name
gender
age
country
UserTagTrack
usertagtrack id
user id
track id
tag id
preferences
It is a general database (default), all files or other databases are created from this.

25. Recommendation Model PrefUserTag user id tag id sum (preferences)
PrefUserTrack
user id
track id
sum (preferences)
PrefTagTrack
track id
tag id
sum (preferences)
In JDBCDataModel, primary keys must be defined because of time efficiency. The database format should be:

26. Number of elements in tables
The name of tables begins with «Pref» statement are formatted table for Mahout recommendation functions.
They contain very low data according to UserTagTrack table.

27. Number of elements in tables
Before the assignment of primary key
With primary key, format is shown below:
user id
tag id
sum (preferences)

28. The introduction of system
After the text file is created via API, standard line of text is shown as follows:
This line represents on UserTagTrack table:
user name, artist name, track name, published year, tags
user_000103, Super Furry Animals, The Undefeated, 2003, indie, britpop, rock, trumpet, pop
usertagtrackid
user id
track id
tag id
preferences 1
user_000103
The Undefeated
indie 20 2
britpop 3
rock 4
trumpet 5
pop

29. The functions used in the recommendation engine
The working principle of user-based recommendation engine:

30. Recommendation Results
The infinite amount of results can be obtained via evaluator program. In thesis, pages have many results with different conditions.
Table Name
PrefUserTag
Neighbourhood Size 2
For User Id 5
# Recommendations
Results
Tag-Name
RecommendedItem[item:112040,value: ]
missjudy76
RecommendedItem[item:3387, value: ]
my 750 essential songs
RecommendedItem[item:8124, value: ]
lionel richie
RecommendedItem[item:8147, value: ]
leona lewis
RecommendedItem[item:1809, value: ]
better than the original

31. Recommendation Results
Table Name
PrefUserTrack
Neighbourhood Size 2
For User Id 5
# Recommendations
Results
Track Name
RecommendedItem[item:7064,value:73.0]
Out Of Control
Neighbourhood Size 7
Results
Track Name
RecommendedItem[item:16570,value:304.5]
When You'Re Gone
RecommendedItem[item:7064, value:73.0]
Out Of Control
RecommendedItem[item:1466, value:9.0]
Aerodynamic
RecommendedItem[item:7170, value:5.0 ]
Bring Me To Life
RecommendedItem[item:2969, value:5.0]
Number Five With A Bullet

32. How to evaluate results ?
The evaluation of this recommendation engine result is realized with the most common metrics precision and recall. 
Precision is calculated with the ratio of relevant items recommended correctly to the number of items recommended.
Recall is the ratio of relevant items recommended correctly to the number of items which are relavent to users.
Actual Positive
Actual Negative
Predicted as positive TP FP
Predicted as negative FN TN

33. How to evaluate results ?
The precision-recall is provided RecommenderIRStatsEvaluator class in Mahout. The evaluate function gives the result of F-measure, precision, recall value of recommendation engine .
Parameters are given this functions, the important parameter is «at» which means that the number of recommendations to consider when evaluating precision
precision at something (integer value)

34. Evaluation Results Table Name PrefUserTag Data Model Structure
User-Tag-Preference
Row-Column Variable Number
# users: 700 , # item: 14044
Neighbourhood Size 2
5 recommendations
Precision:
Recall:
Table Name
PrefUserTrack
Data Model Structure
User-Track-Preference
Row-Column Variable Number
# users: 700, # item:
Neighbourhood Size 2
5 recommendations
Precision:
Recall:

35. User-Track-Preference Row-Column Variable Number
Evaluation Results
Table Name
PrefUserTrack
Data Model Structure
User-Track-Preference
Row-Column Variable Number
# users: 700, # item:
Neighbourhood Size 3
5 recommendations
Precision:
Recall:

36. The comment of evaluation results
If the number of neighbourhood size increases, the recommendation engine results will be better because of the working principle of similarity function.
User-tag recommendation engine is the better than user-track recommendation engine because of data size and sparsity.
People with similar characteristics are also similar musical tastes.
When the neighbourhood size increases, the number of recommended items increases.

37. Self-criticism I
The creation of data set and data representation took a long time. Thus, ready dataset can be used and this way buys project holder extra time.
There are huge amount of data in data model. Scanning all data and making recommendation took a long time because of computer capacity. Thus, I could get a better computer.
The out of memory error was the most frequently encountered problems while calculating evaluation result because of low JAVA heap-space in operating system or Java version.

38. Self-criticism II
Slowness or memory error problems can be solved via using parallel programming. In addition, using server is the another alternative solution for problems.
User-Track Profile results is not good, recommendation engine performance for this model could be increased.
If the computer capacity increases,
more data can be used for
recommendation engine.

39. Thank you for listening 