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Hybrid music recommender using content-based and social information

Paulo Esteban Chiliguano Torres

Submitted for the degree of Master of Science Queen Mary, University of London 2015

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Hybrid music recommender using content-based

and social information

Paulo Esteban Chiliguano Torres

Abstract

This document demonstrates the document class qmwphd, a collection of Latex macros that formats theses to comply with Senate House instructions. According to these instuctions, the title-page should followed by an abstract consisting of no more than 300 words.

An extra copy of the abstract, typed on a special form provided with the entry form, is required for publication in the ASLIB Abstracts of Theses. As yet, qmwphd does not produce an extra copy of the abstract.

Submitted for the degree of Master of Science

Queen Mary, University of London

2015

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Contents

1 Introduction 11 1.1 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 Background 13

2.1 Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 Content-based Recommender Systems . . . . . . . . . . . . . . . . . . . 13 2.1.2 Collaborative filtering Recommender System . . . . . . . . . . . . . . . 13 2.1.3 Hybrid Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 Online Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.1 APIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 Data Fusion Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3 Main contribution 15

3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.1 Content based modelling . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.2 Collaborative filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 Deep Belief Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4 Experiments 17 4.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Results 19

6 Conclusion 21

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List of Figures

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Acknowledgements

This section is not required by Senate House, but I’ve included an example. I’ve put it after the tables because Senate House says the absract should be followed by a table of contents and lists of tables, etc.

I would like to thank my cat and everyone else who relies on me for food.

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Chapter 1

Introduction

Recommender systems can be described as systems that guide users to interesting objects in a huge space of information. In order to achieve high performance, there is the need of hybridization of two or more recommendation techniques.

This project is going to examine a different approach to develop a hybrid music recommender system in order to suggest audio tracks that would be appealing and enjoyable to the user. This system will combine two recommendation techniques. The first one is content-based technique to obtain acoustical features and metadata (ID3) from a music tracks library, and the second one would be a collaborative filtering technique to recognize music preferences on the basis of users social media profiles e.g. Twitter, Facebook, Last.fm.

Deep learning algorithms, such as Convolutional Restricted Boltzmann Machines (CRBMs) or Convolutional Deep Belief Networks (CDBNs), will be considered to extract features from audio files and for music clustering. Social network information will be obtained by crawling user information via Application Program Interface (API) available in many online social networks. These two sources of related data will be integrated considering data fusion techniques.

The evaluation of the system will be assessed by comparing the results obtained from the hybrid recommender system and the results obtained from a single source based recommender system.

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1.1 Outline of the thesis

Chapter 2 covers an introduction to recommender systems, online social networks and information fusion.

Chapter 3 addresses the design of the hybrid recommender system.

Chapter 4 evaluates the performance of the hybrid recommender system.

Chapter 5 describes the results about the application of the hybrid recommender system. The performance is compared to a non-hybrid recommender system based.

Chapter 6 presents the conclusion and some thoughts for further research.

Chapter 2

Background

2.1 Recommender Systems

Recommender systems are software or technical facilities that provide items suggestions or predict individual preferences. These systems play an important role in commercial applications to increase sales and user satisfaction.

Recommender systems can be classified in two groups: Content-based systems and Collaborative filtering systems. [?]

2.1.1 Content-based Recommender Systems Content-based recommender systems compute similarities between content of items.

2.1.2 Collaborative filtering Recommender System In collaborative filtering, similarities are computed between users who have rated similar items.

2.1.3 Hybrid Recommender Systems Hybrid recommendation is based on the combination of techniques mentioned above, by using the advantages of one system to compensate the disadvantages of the other system.

2.2 Online Social Networks

Social network sites (SNSs) are web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with

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whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system (boyd and Ellison, 2007, p. 211).

2.2.1 APIs The publicly available music related information can be collected from user profiles on social networks using Application Program Interface (API).

2.3 Data Fusion Techniques

Combination of multiple sources of information to obtain more relevant parameters is known as data fusion. In this study, a cooperative data fusion technique is considered to augment information provided from social network source to content-based system features.

Chapter 3

Main contribution

3.1 Methods

3.1.1 Content based modelling Classifier creates a model for each user based on the acoustic features of the tracks that user has liked.

3.1.2 Collaborative filtering At this stage, similarities between users is calculated to form a neighbourhood and predict user rating based on combination of the ratings of selected users in the neighbourhood.

3.2 Algorithms

3.2.1 Deep Belief Networks Deep belief network is a probabilistic model that has one observed layer and several hidden layers. Convolutional Deep Belief Network (CDBN)

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Chapter 4 Experiments

4.1 Performance

The performance of the hybrid system is evaluated by comparing the ranking task with the same task of the content-based system.

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Chapter 5 Results

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Chapter 6 Conclusion

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