Music Intelligent Agents: A business model?

EUROPEAN BUSINESS SCHOOL
International University Schloss Reichartshausen

Department of Information Systems

Seminar Paper
WS 2003/2004

Music Intelligent Agents: A business model?

Name:
Marc Dominick
Due date: 10 October 2003

Table of Contents

Table of Abbreviations     ...  II

1 Introduction     ...  1

1.1 Basic problem and objective      ...  1
1.2 Methodology and outline      ...  1

2 Basic concepts and definitions      ...  2

2.1 Intelligent agents     ...  2
2.2 Recommender systems and information filtering      ...  3

3 A Business Model for Music Intelligent Agents      ...  4

3.1 Value proposition     ...  4
3.1.1 Inconsistencies in the music market     ...  5
3.1.2 Overview of the solution and benefit      ...  5
3.2 Architecture of value creation     ...  6
3.2.1 Creating user models     ...  6
3.2.2 Recommendation interface and learning from user feedback      ...  7
3.2.3 Filtering and recommendation technique     ...  8
3.2.4 Software implementation      ...  8
3.3 Revenue sources     ...  10
3.3.1 Content provider      ...  10
3.3.2 Consumer      ...  11
3.3.3 Market research data and targeted marketing      ...  12

4 Further implications of the model      ...  13

4.1 Technical limitations and challenges      ...  13
4.2 Security and social issues     ...  14

5 Conclusion      ...  15

Appendix     ...  17
References     ...  22

Table of Abbreviations
B2B = business to business
B2C = business to consumer
CF = collaborative filtering
EC = electronic commerce
e-tailer = electronic retailer
GUI = Graphical User Interface
IA = intelligent agent(s)
MIA = Music Intelligent Agent
MIT = Massachusetts Institute of Technology
MP3 = MPEG Layer 3
P2P = Peer-to-Peer
PC = Personal Computer
RS = recommender system(s)

1 Introduction

1.1 Basic problem and objective

The rise of the Internet brought disintermediation effects to a huge number of industries of which the music industry is maybe the most prominent example. How technology changed the rules of this world-wide multi-billion dollar business became particularly evident mostly because of the important legal aspects involved.

Record companies had to put up with huge losses, and now on a sudden four years are gone since Napster sparked the P2P revolution in 1999.¹ The increasing digitization of music and concomitant changes in music production and distribution are not reversible anymore. Astonishingly, record companies have not yet come up with models or strategies that use the advantages of this process successfully. Any attempt failed so far because they could not convince the most important stakeholder – the consumer.²

This paper concentrates on intelligent agent technology which has been promisingly described as “[..] software that is proactive, personalized, and adapted. Software that can actually act on behalf of people, take in itiative, make suggestions [..]”³. With more than 4 million existing music titles⁴ music listeners have increasingly problems to discover and select artists or songs they really like. Consequently, they tend to take (mostly in MP3 format) what they can get, be it legal or not, and hope to find a ‘jewel’ among the songs. Intelligent agents in the form of recommender systems pick up the idea of personalization which lacks application in the area of music compared to other indu stries and might help to mitigate the dilemma of the music industry.

Therefore the objective of this paper is to develop a business model based on intelligent agent architecture to automatically provide and recommend music that meets the individual consumer’s taste addressing both business and technical aspects.

1.2 Methodology and outline

This paper is structured as follows. Chapter two focuses on technical aspects and describes the most important definitions and concepts, and thus constitutes the theoretic basis for the subsequent chapters. It begins by explaining intelligent agents in a general context. Then recommender systems, an application field of intelligent agents, are explained. In order to describe recommender systems properly, it is necessary to address and explain the issue of information filtering. Finally, two information filtering techniques that are frequently used in recommender systems are briefly discussed.

In chapter three, which constitutes the main part of the paper, a business model for a music recommender system based on intelligent agent technology is developed. The business model is subdivided into three parts. First, the model is introduced and it is explained which benefit could be reaped by the various parties. Second, by defining the architecture of the system the process of value creation is described and the implications for directly involved stakeholders are explained. Third, potential revenue sources are analysed and involved business aspects examined.

Chapter four explains further implications of recommender systems. A selection of technical limitations and challenges is described and related to the previously developed business model. Additionally, security and social issues that are critical for the success of the model are discussed.

Chapter five summarizes the important findings of the study and closes with concluding thoughts.

2 Basic concepts and definitions

2.1 Intelligent agents

Intelligent agents (IA) also referred to as software agents and bots⁵, are electronic pendants to human agents in form of software programs. Just like travel agents or any other person that is commissioned by a customer, IA autonomously execute tasks on behalf of another party in a computing environment. These tasks usually require specialist know ledge and consist of a time consuming process.⁶ In the networked information age IA are increasingly applied to mitigate the problem of information overload and hence, they are also strongly related to the fields of information filtering and data mining.⁷

Already in 1995 assessed as one of “the fastest growing areas of research and new application development on the Internet”⁸, IA today are vastly commercially used and help supply and demand get together more efficiently. In the B2C sector they e.g. support the consumer’s purchasing decision by comparing characteristics and prices of products (Mysimon.com; Compare.net; Edgegain.com), or as a recommender system IA suggest a product that ought to meet the consumer’s preferences (Findgift.com; Amazon.com). In the field of B2B, e.g. with procurement, IA interact and negotiate autonomously with each other on behalf of their ‘client’-companies.⁹

The exact definition, however, of what can be named an ‘intelligent agent’ is not clear among users and researchers and there exist various types of applications which claim to incorporate IA technology. Especially the prefix ‘intelligent’ evokes controversy since it is the decisive factor that distinguishes IA from ‘normal’ software programs and search engines.¹⁰ In addition to the ‘seek and match’ capabilities of search engines, IA can monitor and proactively take action on movements on a Website or any other environment without the intervention of the user. So called learning agents are able to learn the individuals’ preferences and to make suggestions.¹¹

According to NISSEN, the element of ‘intelligence’ of an agent is the ability for adaptive reasoning which implies the “[..] capability to process information from external environments -- such as networks, databases, and the Internet -- given a set of knowledge, attitudes, and beliefs of the user which are understood by the agent”.¹² One successful type of application of IA are the so called recommender systems which will be presented in the next section. 2.2 Recommender systems and information filtering Recommender systems (RS) employ information filtering algorithms in order to provide product recommendations or suggest objects of interest to users.¹³

[...]

1 See Brislen (2003); there is controversy to what degree digital piracy is responsible for decline in sales.

2 See Rojas (2003), the computer manufacturer Apple recently came up with an internet music platform (iTunes Music Store) that is accepted and successful.

3 Maes (1997), p. 11.

4 See Pachet / Roy / Cazaly (2000), p. 44.

5 See Patton (1999).

6 See Brenner / Zarnekow / Wittig (1998), p. 22; Nissen (1995).

7 See Turban et al. (2002), pp. 153-154.

8 Nissen (1995).

9 See Turban et al. (2002), p. 94 & pp. 156-158 & p. 250.

10 See Brenner / Zarnekow / Wittig (1998), p. 21; Turban et al. (2002), pp. 154-155; .Nissen (1995) is even more precise, he differentiates between programs, (software) agents and intelligent agents.

11 See Turban et al. (2002), pp. 154-160; Nissen (1995); Brenner / Zarnekow / Wittig (1998), pp. 25-29; Maes (1997), p. 11 & p. 17.

12 Nissen (1995).

13 See Mukherjee (2001), p. 114; Sarwar et al. (2000) p.158.