Abstract—Music is an integral part of our life. People listen to music everyday as per their taste and mood. With the advancement and increase in volume of digital content,the choice for people to listen to diverse type of music has also increased significantly. Thus, the necessity of delivering the most suited music to the listeners has been an interesting field of research in computer science. One of the important measures to deliver the best music to listeners could be their personality traits. In order to determine the personality traits of a person, social media like Facebook can be a useful platform where people express their views on different matters, share their opinions and thoughts. This paper first describes the use of Naive Bayes classifier to determine the standard Big Five Personality Traits of a person based on their status updates on Facebook profile using basic natural language processing techniques, and then proceeds to present the use of thus obtained information about personality traits to enhance the widely implemented user-to-user collaborative filtering techniques for music recommendation.

Index Terms—Recommender System, Collaborative Filtering, Personality Traits, Naive Bayes, Music

I. INTRODUCTION
A. Background On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliverrelevantinformationinordertoalleviatetheproblemof information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated informationtoprovideuserswithpersonalizedcontentandservices. Besides, these days social networks have become widely used and popular medium for information dissemination as well as the facilitators of social interactions. User contribution and activities provide a valuable insight into individual behavior, experiences, opinions and interests. Considering that personality, which uniquely identifies each one of us, affects a lot of aspects of human behavior, mental process and affective reactions, there is an enormous opportunity, for adding new personality based qualities in order to enhance the current collaborative filtering recommendation engine. The Big Five Model or Five Factor Model of personality dimensions has emerged as one of the most well-researched and well-regarded measures of personality structure in recent years [2]. The model five domains of personality: Openness, Conscientiousness, Extroversion, Agreeableness and Neuroticism, were conceived by Tupes and Christal [3] as the fundamental traits that emerged from analyses of previous personality tests. McCrae, Costa and John [4] continued five factor model research and consistently found generality across age, gender and cultural lines. The Big Five Model traits are characterized by the following:

• Openness to Experience: Openness is a general appreciation of art, emotion, adventure, unusual ideas, imagination, curiosity, and variety of experience.
• Conscientiousness: Conscientiousness is a tendency to display self-discipline, act dutifully and strive for achievement against measures or outside expectations.
• Extraversion: Extraversion is characterized by breadth of activities, surgency from external activity/situations and energy creation from external means.
• Agreeableness: The agreeableness trait reflects individual differences in general concern for social harmony.
• Neuroticism: Neuroticism is the tendency to experience negative emotions, such as anger, anxiety or depression.

Authors : Abhishek Paudel, Brihat Ratna Bajracharya, Miran Ghimire, Nabin Bhattarai, Daya Sagar Baral ,Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal

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