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DESCRIPTIONthis document provides information on recommender systems for music data using genetic algorithm.Recommender Systems are widely used in e commerce websites.
IntroductionChapter 1IntroductionWhen 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 users preference is a valuable asset for such systems.With the explosion of network in the past decades, internet has become the major source of retrieving multimedia information such as video, books, and music etc. People have considered that music is an important aspect of their lives and they listen to music, an activity they engaged in frequently. Previous research has also indicated that participants listened to music more often than any of the other activities (i.e. watching television, reading books, and watching movies). Music, as a powerful communication and self-expression approach, therefore, has appealed a wealth of research. However, the problem now is to organise and manage the millions of music titles produced by society. MIR(Music Information Retrieval) techniques have been developed to solve problems such as genre classification, artist identification, and instrument recognition. Additionally, music recommender is to help users filter and discover songs according to their tastes. A good music recommender system should be able to automatically detect preferences and generate playlists accordingly. Meanwhile, the development of recommender systems provides a great opportunity for industry to aggregate the users who are interested in music. More importantly, it raises challenges for us to better understand and model users preferences in music.Currently, based on users listening behaviour and historical ratings, collaborative filtering algorithm has been found to perform well. Combined with the use of content-based model, the user can get a list of similar songs by low-level acoustic features such as rhythm, pitch or high-level features like genre, instrument etc.
1.1 AIM AND OBJECTIVESUsing this idea we propose a method to automatically recommend music in a users device as the next song to be played. In order to keep small the computation time for calculating recommendation, the method is based on user behavior and high-level features but not on content analysis. Which song should be played next can be determined based on various factors.
1.2 PROJECT PROBLEM & RELEVANT KNOWLEDGEA Recommender system for music data is 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 analysed using content Based filtering and interactive genetic algorithm. After acquiring records, the system recommends items Appropriate to users own favourite.
High recommendation accuracy Rich variety of artists Prompt responses and adaptation to changing preferences Enriched user interface Improved overall music listening experience
As users accumulate digital music in their digital devices, the problem arises for them to manage the large number of tracks in their devices. If a device contains thousands of tracks, it is difficult, painful, and even impractical for a user to pick suitable tracks to listen to without using pre-determined organization such as playlists. The topic of this project is computationally generated recommendations. Music recommendation is significantly different from other types of recommendations, such as those for movies, books and electronics. Because a same song can be recommended to a same users many times if we successfully keep from him/her bored with it.A main purpose of a music recommendation system is to minimize users effort to provide feedback and simultaneously to maximize the users satisfaction by playing appropriate song at the right time. Reducing the amount of feedback is an important point in designing recommendation systems, since users are in general lazy. We can evaluate users attitude towards a song by examining whether the user listens to the song entirely, and if not, how large a fraction he/she does.In particular, we assume that if the user skips a recommended song, it is a bad recommendation, regardless of the reason behind it. If the recommended song is played completed, we infer that the user likes the song and it is a satisfying recommendation. On the other hand, if the song is skipped while just lasting a few seconds, we conclude that the user dislikes the song at that time and the recommendation is less effective.Using this idea we propose a method to automatically recommend music in a users device as the next song to be played. In order to keep small the computation time for calculating recommendation, the method is based on user behaviour and high-level features but not on content analysis. Which song should be played next can be determined based on various factors.
1.3 PROPOSED SYSTEMWe propose a system to recommend songs to the user based on the users preference. The user will login to his/her account in the system, and listen to songs and give rating to these songs. Once the user clicks the submit button, the system selects two songs with the highest ratings and submits these as input to the genetic algorithm which computes the songs most similar to the songs given as input. These songs are then provided to the user as recommendations.
1.4 SCOPE OF THE PROJECT More than half the music now-a-days is downloaded The trend is bound to rise exponentially Virtually impossible to go through the heap of data and choose Recommendations from primary sources are too narrow They amount to a bulk of online sales across sectors These systems are attracting huge attention and investments from e-commerce sites The proposed system based on concrete analysis and thus is way better than any other conventional methods We propose a real-time genetic recommendation method for music data in order to overcome the shortfalls of existing recommendation systems based on content based filtering and other such techniques that fail in reflecting in the current user preferences.
1.5 ORGANIZATION OF REPORTIn Chapter 2 we have introduced and described existing systems for music recommendation system using genetic algorithms. Chapter 3 deals with object oriented analysis and design.Chapter 4 deals with the implementation of the system, the software and hardware requirements of the system, analysis of the system and the risks associated with developing the system.Chapter 5 includes the project schedule which consists of the timeline chart along with the task sheet.Chapter 6 includes the algorithms and flowcharts used in the system along with system snapshots.Chapter 7 deals with the testing of the system and consists of test cases.Chapter 8 evaluates the project and discusses the applications of the project and last but not the least publications and references are mentioned at the end.
Chapter 2REVIEW OF LITERATUREIntroduction
Many different approaches have been applied to the basic problem of making accurate and efficient recommender and data mining systems. Many of the technologies used in the actual recommender systems studied are fairly simple database queries. Automatic recommender systems, however, use a wide range of techniques, ranging from nearest neighbour algorithms to Bayesian analysis. The worst-case performance of many of these algorithms is known to be poor. However, many of the algorithms have been tuned to use heuristics that are particularly efficient on the types of data that occur in practice.
2.1 Study of Existing Systems
2.1.1 Collaborative Recommender Systems
The earliest recommenders used nearest-neighbour collaborative filtering algorithms. The nearest neighbour algorithms are based on computing the distance based on their preference history. Predictions of how much a consumer will like a product are computed by taking the weighted average of the opinions of a set of nearest neighbours for that product. Neighbours who have expressed no opinion on the product in question are ignored. Opinions should be scaled to adjust for differences in ratings tendencies between users. Nearest neighbour algorithms have the advantage of being able to rapidly incorporate the most up-to-date information, but the search for neighbours is slow in large databases. Practical algorithms use heuristics to search for good neighbours and may use opportunistic sampling when faced with very large populations.
Collaborative recommender systems, as presented by Xerox PARCs researchers consisted on the simple idea that the system would take user-defined news filter rules, compare them, and propose new rules to users. This proposal, however, required a significant amount of users attention and involvement since the new filters had to be added manually by the system users. An alternative method for building a Music Recommender System that combined information about preferences submitted by users to generate automatically filtering rules that were applied on objects in the database, without user explicit intervention.
User Prediction ComputationUser