Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
An industrial-strength content-based music recommendation system
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems
Proceedings of the 2007 ACM conference on Recommender systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Proceedings of the fifth ACM conference on Recommender systems
Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Measuring the validity of peer-to-peer data for information retrieval applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
ACM Transactions on Interactive Intelligent Systems (TiiS)
Bisociative music discovery and recommendation
Bisociative Knowledge Discovery
Modeling the uniqueness of the user preferences for recommendation systems
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
This paper presents some experiments to analyse the popularity effect in music recommendation. Popularity is measured in terms of total playcounts, and the Long Tail model is used in order to rank music artists. Furthermore, metrics derived from complex network analysis are used to detect the influence of the most popular artists in the network of similar artists. The results from the experiments reveal that---as expected by its inherent social component---the collaborative filtering approach is prone to popularity bias. This has some consequences on the discovery ratio as well as in the navigation through the Long Tail. On the other hand, in both audio content--based and human expert--based approaches artists are linked independently of their popularity. This allows one to navigate from a mainstream artist to a Long Tail artist in just two or three clicks.