Music Recommender System Final

ABSTRACT: With growing popularity of the web-based systems that are applied in many different areas, they tend to deliver Customized information for their users by means of utilization of recommendation methods. The recommendation Techniques: content-based, collaborative and by means of genetic algorithm are used in existing systems. They recommend user those items which are based on past history of the user but user’s intention is not focused. This Paper proposes Genetic algorithm for recommendation by generating new mutations by providing optimized solution every time and which is based on user’s preferences.

Keywords: Recommender system, Genetic algorithm, User Profiling, Content Based filtering

Introduction: When users browse through a web site they are usually looking for items they find interesting. Interest items can consist of a number of things. For example, textual information can be considered as interest items or an index on a certain topic could be the item a user is looking for.

Another example, applicable for a web vendor, is to consider purchased products as interest items. Whatever the items consist of, a website can be seen as a collection of these interest items. Recommender systems are widely implemented in e-commerce websites to assist customers in finding the items they need.

A recommender system should also be able to provide users with useful information about the item that interest them. The ability of promptly responding to the changes in user’s preference is a valuable asset for such systems.

A Recommender system for music data it proposed which assists customers in searching music data and provides result with items resulting in own user preference. This system first extracts unique properties of music like pitch, chord, and tempo from the music file using a CLAM annotator software tool. This extracted data is then stored on the database. Each stored property is analyzed using content Based filtering and interactive genetic algorithm. After acquiring records, the system recommends items Appropriate to user’s own favourite.

Literature Survey What is Recommender system? Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item (such as music, books, or movies) or social element (e.g. people or groups) they had not yet considered, using a model built from the characteristics of an item (content-based approaches). Recommendation Systems forms a specific type of information filtering technique that attempts to present information items. (like movies, music, books, news, images, etc.) A Recommender system for music data is proposed which assists customers in searching music data and provides result with items resulting in own user preference. Recommender systems have become extremely common in recent years.

Fig : Recommender System

What is Content based filtering? A common approach when designing recommender systems is content-based filtering. Content-based filtering methods are based on information about and characteristics of the items that are going to be recommended. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past (or is examining in the present). In particular, various candidate items are compared with items previously rated by the user and the bestmatching items are recommended. >> key issue with content-based filtering is whether the system is able to learn user preferences from user's actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended.

Fig : Content based Approch

Technical Requirements

HARDWARE 1. 256 MB RAM. 2. 80 GB HDD. 3. Intel 1.66 GHz Processor Pentium 4

SOFTWARE 1. Visual Studio 2008(.Net framework) 2. MS SQL Server 2005

Technology Used

.NET Framework The .NET Framework is an environment for building, deploying, and running XML Web services and other applications. It is the infrastructure for the overall .NET platform. The .NET Framework consists of three main parts: the common language runtime, the class libraries, and ASP.NET. The common language runtime and class libraries, including Windows Forms, ADO.NET, and ASP.NET, combine to provide services and solutions that can be easily integrated within and across a variety of systems. The .NET Framework provides a fully managed, protected, and feature-rich application execution environment, simplified development and deployment, and seamless integration with a wide variety of languages.

ASP.NET ASP.NET is more than the next version of Active Server Pages (ASP); it is a unified Web development platform that provides the services necessary for developers to build enterpriseclass Web applications. While ASP.NET is largely syntax-compatible with ASP, it also provides a new programming model and infrastructure that enables a powerful new class of applications. You can migrate your existing ASP applications by incrementally adding ASP.NET functionality to them. ASP.NET is a compiled .NET Framework -based environment. You can author applications in any .NET Framework compatible language, including Visual Basic and Visual C#. Additionally, the entire .NET Framework platform is available to any ASP.NET application. Developers can easily access the benefits of the .NET Framework, which include a fully managed, protected, and feature-rich application execution environment, simplified development and deployment, and seamless integration with a wide variety of languages.

Microsoft SQL Server Business today demands a different kind of data management solution. Performance scalability, and reliability are essential, but businesses now expect more from their key IT investment. SQL Server 2005 exceeds dependability requirements and provides innovative capabilities that increase employee effectiveness, integrate heterogeneous IT ecosystems,and maximize capital and operating budgets. SQL Server 2005 provides the enterprise data management platform your organization needs to adapt quickly in a fast changing environment. Benchmarked for scalability, speed, and performance, SQL Server 2005 is a fully enterprise-class database product, providing core support for Extensible Markup Language (XML) and Internet queries.

Easy-to-use Business Intelligence(BI) Tools Through rich data analysis and data mining capabilities that integrate with familiar applications such as Microsoft Office, SQL Server 2005 enables you to provide all of your employees with critical, timely business information tailored to their specific information needs. Every copy of SQL Server 2005 ships with a suite of BI services.

Self-Tuning and Management Capabilities Revolutionary self-tuning and dynamic self-configuring features optimize database performance, while management tools automate standard activities. Graphical tools and performance, wizards simplify setup, database design, and performance monitoring, allowing database administrators to focus on meeting strategic business needs.

Data Management Application and Services Unlike its competitors, SQL Server 2005 provides a powerful and comprehensive data management platform. Every software license includes extensive management and development tools, a powerful extraction, transformation, and loading (ETL) tool, business intelligence and analysis services, and analysis service, and such as Notification Service. The result is the best overall business value available. Enterprise Edition includes the complete set of SQL Server data management and analysis features are and is uniquely characterized by several features that makes it the most scalable and available edition of SQL Server 2005 .It scales to the performance levels required to support the largest Web sites, Enterprise Online Transaction Processing (OLTP) system and Data Warehousing systems. Its support for failover clustering also makes it ideal for any mission critical line-of-business application. Additionally, this edition includes several advanced analysis features that are not included in SQL Server 2005 Standard Edition.

Requirement Analysis FEASIBILITY STUDY The very first phase in any system developing life cycle is preliminary investigation. The feasibility study is a major part of this phase. A measure of how beneficial or practical the development of any information system would be to the organization is the feasibility study. The feasibility of the development software can be studied in terms of the following aspects:

1. Operational Feasibility. 2. Technical Feasibility. 3. Economical feasibility. 4. Motivational Feasibility. Legal Feasibility

OPERATIONAL FEASIBILITY The site will reduce the time consumed to maintain manual records and is not tiresome and cumbersome to maintain the records. Hence operational feasibility is assured.

TECHNICAL FEASIBILITY Minimum hardware requirements:  At least 166 MHz Pentium Processor or Intel compatible processor.  At least 256 MB RAM.  14.4 kbps or higher modem.  A video graphics card.  A mouse or other pointing device.  At least 1 Gb free hard disk space.  Microsoft Internet Explorer 4.0 or higher.

ECONOMICAL FEASIBILTY Once the hardware and software requirements get fulfilled, there is no need for the user of our system to spend for any additional overhead. For the user, the web site will be economically feasible in the following aspects:  The web site will reduce a lot of paper work. Hence the cost will be reduced.  Our web site will reduce the time that is wasted in manual processes.  The storage and handling problems of the registers will be solved.

MOTIVATIONAL FEASIBILITY The users of our system need no additional training. Visitors do not require entering password and are shown the appropriate information.

LEGAL FEASIBILITY The licensed copy of the required software is quite cheap and easy to get. So from legal point of view the proposed system is legally feasible. Software Development Model Used: Software process model deals with the model which we are going to use for the development of the project. There are many software process models available but while choosing it we should choose it according to the project size that is whether it is industry scale project or big scale project or medium scale project. Accordingly the model which we choose should be suitable for the project as the software process model changes the cost of the project also changes because the steps in each software process model varies.

This software is build using the waterfall mode. This model suggests work cascading from step to step like a series of waterfalls. It consists of the following steps in the following manner

Waterfall model: Analysis Phase

Design Phase

Coding Phase

Testing Phase

Analysis Phase: To attack a problem by breaking it into sub-problems. The objective of analysis is to determine exactly what must be done to solve the problem. Typically, the system’s logical elements (its boundaries, processes, and data) are defined during analysis. Design Phase: The objective of design is to determine how the problem will be solved. During design the physical data structures, screens, reports, files and databases. Coding Phase: The system is created during this phase. Programs are coded, debugged, documented, and tested. New hardware is selected and ordered. Procedures are written and tested. End-user documentation is prepared. Databases and files are initialized. Users are trained. Testing Phase: Once the system is developed, it is tested to ensure that it does what it was designed to do. After the system passes its final test and any remaining problems are corrected, the system is implemented and released to the user.

GENETIC ALGORITHM Introduction of GA: In the computer science field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.

Genetic algorithm procedure: 1.

Choose the initial population of individuals

2. valuate the fitness of each individual in that population 3. Repeat on this generation until termination (time limit, sufficient fitness achieved, etc.): 4. Select the best-fit individuals for reproduction 5. Breed new individuals through crossover and mutation operations to give birth to offspring 6. Evaluate the individual fitness of new individuals 7. Replace least-fit population with new individuals

The evolutionary process of a GA is a highly simplified and stylized simulation of the biological version. It starts from a population of individuals randomly generated according to some probability distribution, usually uniform and updates this population in steps called generations. Each generation, multiple individuals are randomly selected from the current population based upon some application of fitness, bred using crossover, and modified through mutation to form a new population. 

Crossover – exchange of genetic material (substrings) denoting rules, structural components, features of a machine learning, search, or optimization problem.

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Selection – the application of the fitness criterion to choose which individuals from a population will go on to reproduce.

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Replication – the propagation of individuals from one generation to the next

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Mutation – the modification of chromosomes.

Genetic algorithm in recommender system: A Recommender system for music data is proposed which assists customers in searching audio data and provides result with items resulting in own user preference. This system first extracts unique properties of music like pitch, chord, and tempo from the music file using a CLAM annotator software tool. This extracted data is then stored on the database. Each stored property is analyzed using content based filtering and interactive genetic algorithm. After acquiring records, the system recommends items appropriate to user’s own favourite. The genetic algorithm is applied and user is provided with a general list from which users can select the audio tracks, listen to it and give rating, recommendation list and user favourite list where that user has given highest rating to the audio tracks

PHASES OF GENETIC ALGORITHM IN RECOMMENDER SYSTEM: Fig: The process of Interactive GA phase

The following are phases of genetic algorithm are as follows:

1. Selection phase – Music features are extracted using CLAM software. The items which are rated above threshold value are selected rest other items are ignored.

2. Crossover phase – The BLX – alpha crossover algorithm is used since extracted features are real numbers. Hence crossover is performed with this algorithm resulting in new generation

BLX-a: 1. Select two parents X(t) and Y(t) from a parent pool 2. Create two offspring X(t+1) and Y(t+1) as follows: 3. for i = 1 to n do 4. di=|xi(t)-yi(t)| 5. Choose a uniform random real number u from 6. interval 8. xi(t+1)=u 9. Choose a uniform random real number u from 10. interval 11. 12. yi(t+1)=u 13. end do 14. where: a – positive real parameter

3. Matching phase- This phase finds the similar items stored in database to the newly Generated music features. Once similarity is found those items are recommended to the User. This phase uses Euclidean distance between two offspring and distance between Each feature of the two offspring is calculated, resulting value is used to match the records Stored in the database those records are compared with the resulting value which the user has given highest rating to the tracks.

Flow of the system

Fig: The Flow of the Recommender System.

The Above Figure explains the flow of the system which can be designed and implemented. The idea is to provide each user with user profile for audio tracks. Each of the records from the data base is displayed on user page and called as general list and indexing can be provided to make it more users friendly. The CLAM software then extracts the features from each of the files which the user has provided rating, the features extracted can be tempo, pitch, etc. These extracted features are stored on to the database. The genetic algorithm is used further to provide optimized solutions for recommending items and recommended list will be always provide newer solutions one which are closer to user’s preference. The genetic algorithm can be made to run infinite times to generate the mutations, hence every time providing new solutions that is, new recommendation every time.

System Architecture

Fig: System Architecture. WORKING: 

Apply genetic algorithm to music recommendation system.

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The system can detect and recommend appropriate songs which are suitable for user’s musical preference.

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And, the system requires pre-processing which is feature (i,e. tempo, chord, pitch, etc.) extraction from music. It based on shuffle operation. (i.e, play song randomly)

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At first time, the system recommends songs randomly and user cans judge’s song’s preference by controlling their devices or program. Just click the next song button.

PROJECT PLAN 5.1 IMPLEMENTATION PLAN

The following table gives the project plan for the Phase 1 & 2 of our project:

Phase 1: Activity

Description

Effort in person weeks

Deliverable

Phase 1

P1-01

Requirement Analysis

2 weeks

P1-02

Existing System Literature

P1-03

Technology Selection

2 weeks

>NET

P1-04

Modular Specifications

2 weeks

Module Description

P1-05

Design & Modelling

4 weeks

Analysis Report

Total

13 weeks

Study

& 3 weeks

Requirement Gathering Existing System Study & Literature

Phase 2: Activity

Description

Effort in person weeks

Deliverable

Phase 2

P2-01

Detailed Design

2 weeks

LLD / DLD Document

P2-02

UI and user interactions design

Included above

P2-03

Coding & Implementation

12 weeks

Code Release

P2-04

Testing & Bug fixing

2 weeks

Test Report

P2-05

Performance Evaluation

4 weeks

Analysis Report

P2-06

Release

Included above

in System Release

Total

20 weeks

in UI document

Deployment efforts are extra

Gantt chart Gantt Charts: The Gantt Chart Shows planned and actual progress for a number of tasks displayed against a horizontal time scale. It is effective and easy-to-read method of indicating the actual current status for each of set of tasks compared to planned progress for each activity of the set. Gantt Charts provide a clear picture of the current state of the project.

CONCLUSION We proposed real-time genetic recommendation method in order to overcome the existing recommendation techniques that are not reflecting in the current user`s intend. With the genetic algorithm newer solutions can be generated providing optimal solution each time when the algorithm is made to run, thus providing mutations. This method can be compared with the existing ones which lack the quality of providing accurate results.

REFERENCES [1] Hyun – Tae Kim, Eungyeong Kim, “Recommender system based on genetic algorithm for music data”, 2nd International Conference on Computer Engineering and Technology, 2010. [2] J. Ben Schafer, Joseph Konstan, John Riedl,”Recommender Systems in ECommerce”, 2007.

[3] D.E Goldberg and J. H Holland, “Genetic Algorithms and Machine Learning”, 2006. [4] Paul resnick and Hal .R. varian,”Recommender system”,Vol. 40, No. 3 COMMUNICATIONS OF THE ACM, March 1997. [5] Tom V. Mathew, “Genetic algorithm”, http://www.civil.iitb.ac.in/tvm/2701_dga/2701- ganotes/gadoc/gadoc.html [6] Xiaoyuan Zhu, Yongquan Yu, Xueyan Guo,”Genetic Algorithm based on Evolution Strategy and the Application in Data Mining”, pp- 849-852, First International Workshop on Education Technology and Computer Science, 2009. [7] Janusz Sobecki,”Implementations of Web-based Recommender Systems Using Hybrid Methods”, International Journal of Computer Science & Applications ,2006 Techno mathematics Research Foundation, Vol. 3 Issue 3, pp 52 – 64.